This paper appeared in Library Trends, Fall 1999.
Acknowledgments: This work was supported by the U.S. Army Artificial Intelligence Center, and by the U. S. Naval Postgraduate School under funds provided by the Chief for Naval Operations. Brief Vita: Biography: Neil C. Rowe is Associate Professor of Computer Science at the U.S. Naval Postgraduate School where he has been since 1983. He has a Ph.D. in Computer Science from Stanford University (1983), and E.E (1978), S.M. (1978), and S.B. (1975) degrees from the Massachusetts Institute of Technology. His main research interest is intelligent access to multimedia databases, and he has also done work on robotic path planning, intelligent tutoring systems, and computer security. He is the author of Artificial Intelligence Through Prolog, Prentice-Hall, 1988 and a forthcoming book on information security.
Multimedia data are increasingly important information resources for computers and networks. Much of the excitement over the World Wide Web is about its multimedia capabilities. Images of various kinds are its most common nontextual data. But finding the images relevant to some user need is often much harder than finding text for a need. Careful content analysis of unrestricted images is slow and prone to errors. It helps to find captions or descriptions as many images have them.
Nonetheless, multimedia information retrieval is still difficult. Many problems must be solved just to find caption information in the hope of finding related images. Only a 1% success rate was obtained in experiments trying to retrieve photographs depicting single keywords like "moon" and "hill" using the Alta Vista search engine on the World Wide Web (Rowe and Frew, 1998). That is since most text on Web pages, and even on pages with well-captioned photographs, was irrelevant to the photographs, and the words searched for had many senses. To improve this performance several things are needed: a theory of where captions are likely to be on pages; a theory of which images are likely to have described content; language-understanding software to ascertain the correct word senses and their interrelationships in the caption candidates; image-understanding software to obtain features not likely to be in the caption; a theory connecting caption concepts to image regions; and efficient methods of retrieval of relevant images in response to user queries or requests. Note that speed is only critical in the last phase; while efficient methods are always desirable, accuracy is more a concern because it is so low for keyword-based retrieval. The time to do careful language and image processing can be justified during the indexing of a database if it can significantly improve later retrieval accuracy.
Consider Figure 1, which shows part of a U.S. Army Web page. Much
text is scattered about, but not all of it refers to the pictures.
The two formal captions (in italics, but not otherwise identified) are
inconsistently placed with respect to their photographs. But the
title "Gunnery at Udairi" is a caption too. Next, note that many of
the words and phrases in these candidate captions do not describe the
pictures. Neither picture shows "U.S. Army Central Command", "power
generator equipment", an "Iraqi", "the Gulf War", or "Fort Hood";
matching would falsely retrieve this page for any of these keyphrases.
Similarly, the words "commander", "senior", "signal", "fire",
"target", and "live" are all used in special senses, so this page
would be falsely retrieved for queries intending to refer to their
most-common senses. The only way to eliminate such errors is to parse
and interpret caption candidates using detailed linguistic knowledge.
Finally, note that many things seen in both photographs are not
mentioned in their captions. Only a small part of the left photograph
area is devoted to the people, and the right photograph displays many
features of the tanks not mentioned in its caption. Thus there are
many challenges in indexing the multimedia data on these pages.
Figure 1: Example Web page.
Noteworthy current systems for image retrieval are QBIC (Flickner et al, 1995), Virage (Virage Inc., San Mateo, California, USA), and VisualSEEK (Smith and Chang, 1996), which exploit simple-to-compute image properties like average color and texture. The user specifies color and texture patches, or perhaps an entire image, which is then compared to the images in the database to find the best matches. But these systems strongly emphasize visual properties and can confuse very different things of accidentally similar appearance, like seeing a face in an aerial photograph. So these systems would not help for a typical Web page like Fig. 1 since color similarity to the images there would not mean much. Another category of current image-retrieval systems like Chabot (Ogle, 1995) primarily exploits descriptive information about the image, but all this information must be entered manually for each image by someone knowledgeable about it, which requires a considerable amount of tedious work.
The most interesting current research has focused on knowledge-based multimodal methods for addressing the limitations of current systems. Work on indexing of video (Hauptman and Witbrock, 1997; Smoliar and Zhang, 1994) has achieved success using knowledge-based multimodal analysis of images, image-sequence segmentation, speech, on-screen text, and closed-caption information. For single-image recognition, Piction (Srihari, 1995) does natural-language understanding of the caption of an image, and combines this information with results of face localization in the image, to provide a deeper understanding of an image. But Piction assumed that captions were already isolated for each image, and there are many interesting image features besides faces.
This article summarizes a promising approach that the MARIE project has explored recently using knowledge-based methods for accurate photograph retrieval in response to English queries by a user. The idea is to consider image retrieval in a broader perspective than that of Piction. The subtasks are finding the image, analyzing all relevant text, analyzing the image, mapping the results of the text analysis to the results of the image analysis, and efficient subsequent retrieval of this information. By considering these subtasks as parts of a larger context we will see important issues not addressed by piecemeal efforts.
The methods of MARIE were tested in three partial prototype systems of MARIE-1, MARIE-2, and MARIE-3. These systems primarily address photographs since many users consider them the most valuable multimedia objects, but most of the methods generalize easily. Both explicit photograph libraries (especially the Photo Lab of NAWC-WD, China Lake, California, USA, with its images depicting a wide range of activities at a naval aircraft test facility) and implicit libraries (especially the World Wide Web) were investigated. Most of the code is in Quintus Prolog with some key sections in C. The remainder of the article discusses in turn each of the main problems that MARIE faced.
Identifying captioned images and their captions is a significant problem with book-like multimedia data (as Figure 1). Web images are easy to spot in the HTML page-markup language used. But symbolic graphics of no interest to index are generally stored the same way as photographs, as files in GIF or JPEG format, so a useful system must distinguish them. Recent work with the MARIE-3 system (Rowe and Frew, 1998) has showed that seven quickly-found parameters of images are sufficient to distinguish photographs with 70% recall (fraction of all photographs found) and 70% precision (fraction of actual photographs of those found) on a test set of random Web pages. The parameters are size, squareness, number of distinct colors, fraction of pure colors (white, black, pure gray, red, green, and blue), color variation between neighbor pixels, variety of colors, and use of common non-photograph words (like "button" or "logo") in the name on the image file. The parameters are converted to probabilities by applying "sigmoid" (S-shaped) functions of the form |y sub i = tanh ( ( x sub i - mu ) / sigma )| where |mu| and |sigma| are constants chosen to set the center and steepness of the sigmoid curve. The probabilities are then input to a "linear-classifier neuron" calculating |w sub 0 + w sub 1 y sub 1 + w sub 2 y sub 2 + ... + w sub 7 y sub 7| for a set of weight constants |w sub i| determined by training. If the calculation results in a positive number, the image is considered a photograph. For Figure 1, MARIE-3 rated the left image as 0.244 and the right image as 0.123 after training, so both were correctly classified as photographs.
MARIE-3 then looks for captions around each possible photograph. Captions are not often easy to identify because they take many forms. It is best to work on the HTML source code of the Web page, parsing it to group related things together. Another seven-input linear-classifier neuron with sigmoid functions on its inputs can rate the caption candidates. Its input parameters were easy-to-calculate properties of text: distance in lines from the candidate caption to the image reference, number of other candidates at the same distance, strength of emphasis (e.g. italics), appropriateness of candidate length, use of common (counted positively) or uncommon (counted negatively) words of captions, number of identical words between candidate and either image file name or its nongraphics substitute, and fraction of the words having at least one physical-object sense. Figure 2 shows the caption candidates for Figure 1 with their ratings.
|image4||plaintext||-1||"gunnery at udairi range (3 photos)"|
|image4||plaintext||-1||"red dragon olympics (2 photos)"|
|image4||plaintext||-1||"pillow talk (1 photo)"|
|image4||plaintext||-1||"plotting and planning produces|
|big picture (2 photos)"|
|image4||bold||-2||"table of contents"|
|image4||emphasis||1||"maj general james b. taylor, commander of u.s. army|
|central command forward reenlists staff sergeant danny george,|
|senior power generator equipment repairman of the 385th signal company."|
|image4||heading2||2||"gunnery at udairi"|
|image1||heading2||0||"gunnery at udairi"|
|image1||emphasis||-1||"maj general james b. taylor, commander of u.s. army|
|central command forward reenlists staff sergeant danny george,|
|senior power generator equipment repairman of the 385th signal company."|
|image1||emphasis||1||"m1a1 tanks from a co, 2nd battalion, 12th cavalry|
|regiment fire on an iraqi tank that was destroyed during the gulf war."|
|image1||emphasis||1||"the inoperable tanks are often|
|used as targets on udairi range."|
|image1||emphasis||1||"using iraqi tanks as targets creates a realism during|
|live fire training exercises that can't be duplicated at fort hood."|
|image1||plaintext||3||"4th public affairs detachment"|
The caption neuron by itself showed 21% recall for 21% precision in matching caption-image pairs. This can be improved by combining its rating with the photograph rating since photographs are much more likely to have captions. Neuron outputs can be converted to probabilities and multiplied, with three refinements. Since a survey of random Web photographs showed that 7% had no visible captions, 57% had one caption, 26% had two, 6% had three, 2% had four, and 2% had five, it is reasonable to limit each image to its three best captions. If a caption can go with more than one image, rule out everything but the strongest match, since a useful caption should be precise enough to describe only one image. For example, the "MAJ General" caption candidate in Figure 1 goes better with the left image "image4" (since captions are more often below than above) so the match to "image1" was ruled out; and "Gunnery at Udairi" goes better with the right picture from examination of the HTML code even though it is displayed similarly to the "MAJ General" candidate. Finally, consider possible "invisible" captions: the image file name, the name of any Web page pointed to by the image, any text-equivalent string for the image, and the Web page title, when their likelihoods exceed a threshold.
All this gave 41% recall with 41% precision on a random test set, or 70% recall with 30% precision, demonstrating the value of multimodal evidence fusion. Processing required 0.015 cpu seconds per byte of HTML source code, mostly in the image analysis, and the program consisted of 83 kilobytes of source code. Figure 3 shows the final captions found by MARIE-3 for Figure 1, one for the first photograph and two for the second.
|image4||"MAJ General James B. Taylor, commander of U.S. Army||0.937|
|Central Command-Forward reenlists Staff Sergeant Danny George,|
|senior power generator equipment repairman of the|
|385th Signal Company."|
|image1||"Gunnery at Udairi"||0.954|
|image1||"M1A1 tanks from A co, 2nd Battalion, 12th Cavalry0.929|
|Regiment fire on an Iraqi tank that was destroyed during|
|the Gulf War."|
Figure 3: Final captions inferred by MARIE-3 for the Web page in Figure 1, with their final ratings.
Once likely captions are found, their words can be indexed as keywords for later retrieval (excluding words not nouns, verbs, adjectives, or adverbs). Several information-retrieval systems and Web search tools do this. But retrieval precision will not then be high because a word can have many possible meanings and many possible relationships to neighboring words. Nonetheless, most captions are unambiguous in their context. "View of planes on taxi from tower" is unambiguous in our NAWC-WD Navy-base test captions since "planes" are always aircraft and not levels, "tower" is always a control tower when aircraft are mentioned, "taxi" has a special meaning for aircraft, aircraft are always taxiing and not on top of a taxicab, and the view (not the aircraft or taxiing) is from the tower. Keyword-based retrieval will thus get many incorrect retrievals with the words of this caption. They would furthermore usually miss captions having synonyms or generalizations of the words, like for the caption "Photograph of 747's preparing to takeoff as seen from control" and the query "View of planes on taxi from tower".
Fortunately, true caption language understanding is easier than most text understanding (like automatic indexing of journal articles) since captions must describe something visible. Caption language heavily emphasizes physical objects and physical actions; verbs usually appear as participles, with a few gerunds and past tenses; and words for social interactions, mental states, and quantifications are rare. All this simplifies analysis. Also, many valuable applications involve technical captions, whose accessibility could be valuable to enhance, but whose difficulty primarily resides in code words and unusual word senses that are nonetheless unambiguous, grammatically easy to classify, and often defined explicitly somewhere (e.g. "zeppo" of "zeppo radar").
Many caption words are familiar words of English, and MARIE's parser needs only their parts of speech and superconcepts, obtained from the Wordnet thesaurus system (Miller et al, 1990). For the remaining words, caption writers often try to be clear and follow simple recognizable lexical rules, like that "F-" followed by a number is a fighter aircraft, and any number followed by "kg" is a weight in kilograms. Such rules for the words in 36,191 NAWC-WD captions with 29,082 distinct words covered 17,847 of them while developing MARIE-2 (see Figure 4). Person names, place names, and manufacturer names were obtained in part from existing databases, for a total of 3622 words. 1174 of the remaining words were misspellings, of which 773 were correctly deciphered by our misspelling-detection software (including misspellings of unknown words, by examining word frequencies). 1093 of the remaining words were abbreviations or acronyms, of which 898 were correctly deciphered by our abbreviation-hypothesizing and misspelling-fixing software (Rowe and Laitinen, 1995) using context and analogy. 1876 remaining equipment names were not important to define further. That left 1763 words needing explicit definition, almost all nouns. MARIE-2's 29,082-word lexicon construction required only 0.4 of a man-year, and much of the work can be reused unchanged for other technical applications. So porting MARIE-2 requires some work, but not much.
|Number of captions||36,191|
|Number of words in the captions||610,182|
|Number of distinct words in the captions||29,082|
|Subset having explicit entries in Wordnet||6,729|
|Number of these for which a preferred alias is given:||1,847|
|Number of word senses given for these words:||14,676|
|Subset with definitions reusable from MARIE-1||770|
|Subset that are morphological variants of other known words||2,335|
|Subset that are numbers||3,412|
|Subset that are person names||2,791|
|Subset that are place names||387|
|Subset that are manufacturer names||264|
|Subset that have unambiguous defined-code prefixes||3,256|
|Unambiguous defined-code prefixes:||947|
|Subset that are other identifiable special formats||10,179|
|Subset that are identifiable misspellings||1,174|
|Misspellings found automatically:||713|
|Subset that are identifiable abbreviations||1,093|
|Abbreviations found automatically||898|
|Subset with definitions written explicitly for MARIE-2||1,763|
|Remaining words, assumed to be equipment names||1,876|
|Explicitly used Wordnet alias facts of above Wordnet words||20,299|
|Extra alias senses added to lexicon beyond caption vocabulary||9,324|
|Explicitly created alias facts of above non-Wordnet words||489|
|Other Wordnet alias facts used in simplifying the lexicon||35,976|
|Extra word senses added to lexicon beyond caption vocabulary||7,899|
|Total number of word senses handled||69,447|
|(includes related superconcepts, wholes, and phrases)|
Although captions are usually unambiguous in their subject areas, effort is needed to determine the word senses and word relationships used in a subject area because many of these are atypical of natural language. This information could be laboriously defined manually for each subject area, but a better way is to learn it from context. This can be done with a statistical parser that learns word-sense frequencies and sense-association frequencies from a corpus of examples. A bottom-up chart parser (Charniak, 1993) will suffice. This is natural-language processing software that builds up interpretations of larger and larger substrings of the input word list by always trying to combine the strongest substring interpretations found so far. The strength of an interpretation can reflect the relative frequencies of the word senses used, the frequencies of the word-sense associations made, and the frequencies of the parse rules used.
To simplify matters, restrict the grammar to unary and binary parse rules (rules with only one or two symbols as the replacement for some grammatical symbol). The degree of association between two word strings in a binary parse rule can be taken as the degree of association of the two headwords of the strings. Headwords are the subjects of noun phrases, the verbs of verb phrases, sentences, and clauses, the prepositions of prepositional phrases, etc. So the headword of "F-18 aircraft on a runway" would be "aircraft", and the headword of "on a runway" would be "on". Then a particular caption interpretation can be rated by combining the degrees of association of the headword pairs at every node of its parse tree with a-priori frequencies of the word senses for leaves of the tree. This is a classic problem in evidence fusion, and the simplest solution is to assume independence and multiply the probabilities. It also helps to weight the result by an adjustable monotonically-increasing function of the sentence length, to encourage work on longer subphrases.
However, word-sense association frequencies are often sparse and statistically unreliable. So use "statistical inheritance" to estimate frequencies from more general ones. For instance, to estimate the frequency of "on" as a preposition and "runway" as the subject of its prepositional phrase, look for statistics of "on" and any surface, or if the sample size is not enough, "on" and a physical object. When sufficiently reliable frequency statistics for a modified pair are found, divide by the ratio of the number of occurrences of the substitute word to the number of occurrence of "runway", since a more general concept should associate proportionately more with "on". Similarly, generalize "on" to the class of all physical-relationship prepositions, or generalize both "on" and "runway" simultaneously. Generalize even to the class of all prepositions and the class of all nouns (i.e. the parse-rule frequency) if necessary, but those statistics are less reliable; one should prefer the minimum generalization having statistically reliable data. Statistical inheritance is self-improving, because each confirmed interpretation provides more data.
This statistical approach to parsing addresses the two main linguistic challenges of captions, interpretation of nouns modifying other nouns ("nominal compounds") and interpretation of prepositional phrases. Both involve inference of case relationships. Figure 5 lists important cases for nominal compounds in the captions studied in (Rowe and Frew, 1998). Distinguishing these cases requires a good type hierarchy which Wordnet can supply, as well as providing synonym and part-whole information for word senses.
|lights type iv||object-type|
Figure 5: Important cases of nominal compounds for captions.
The result of parsing and semantic interpretation of a caption is a meaning representation. Since captions so rarely involve quantification, tenses, and hypothetical reasoning, their meaning is generally expressible with "conjunctive semantics", as a list of type and relationship facts. Each noun and verb maps to an instance of its word-sense type, and instances are related with relationship predicates. Figure 6 shows the semantic interpretations found by MARIE-3 for the inferred captions of Figure 1. Multiple captions found for the same image were treated as separate sentences. The "v" symbols are existentially quantified variables for type instances, and hyphenated numbers are sense numbers. Sense numbers 0-49 are for nouns (with nonzero sense numbers from Wordnet version 1.5); 50-99 are for adjectives (with the number minus 50 being the Wordnet sense number); and 100-149 are for verbs (with the number minus 100 being the Wordnet sense number). The "a_kind_of" expressions relate a variable to a type, so for instance "a_kind_of(v435,enlist-103)" says the caption refers to some v435 that is an instance of the verb "enlist" in Wordnet sense number 3. Other expressions give properties of variables, like "quantification(v435,singular)" meaning that the enlisting event was singular as opposed to plural, and two-variable expressions relate the variables, like "object(v435,v14)" meaning that the object of the enlisting was some v14, an instance of "Staff Sergeant".
[a_kind_of(v435,enlist-103), quantification(v435,singular), property(v435,repeated), object(v435,v14), a_kind_of(v14,'Staff Sergeant'-0), a_kind_of(v14,'George'-0), identification(v14,'Danny'), a_kind_of(v14,serviceman-2), subject(v14,v558), a_kind_of(v558,equipment-1), part_of(v558,v486), a_kind_of(v486,generator-2), agent(v464,v486), a_kind_of(v464,power-3), property(v14,senior-51), owned_by(v14,v22), a_kind_of(v22,'Signal Company'-0), property(v22,'385th'-50), rank(v22,385), quantification(v22,the), agent(v435,v11), a_kind_of(v11,'Major General'-0), a_kind_of(v11,'James'-0), a_kind_of(v11,'Taylor'-0), identification(v11,'B.'-0), a_kind_of(v11,commander-2), located_at(v11,v212), a_kind_of(v212,front-1), located_at(v212,v209), a_kind_of(v209,command-0), property(v212,central-52), part_of(v212,v8), a_kind_of(v8,'United States Army'-0)]).
Inferred caption on right photograph: "gunnery at udairi. m1a1 tanks from a co, 2nd battalion, 12th cavalry regiment, fire on an iraqi tank that was destroyed during the gulf war."
[a_kind_of(v1,gunnery-1), at(v1,v15), a_kind_of(v15,'Udairi'-0), during(v1,v18), a_kind_of(v18,shoot-109), quantification(v18,plural), object(v18,v148), a_kind_of(v148,tank-4), property(v148,'Iraqi'-50), quantification(v148,a), object(v156,v148), a_kind_of(v156,destroy-101), tense(v156,past), quantification(v156,singular), during(v156,v21), a_kind_of(v21,'Gulf War'-0), quantification(v21,the), agent(v18,v23), a_kind_of(v23,'M1A1'-0), quantification(v23,plural), from(v23,v49), a_kind_of(v49,company-4), quantification(v49,a), owned_by(v49,v75), a_kind_of(v75,battalion-1), property(v75,'2nd'-51), owned_by(v75,v89), a_kind_of(v89,regiment-1), subject(v89,v85), a_kind_of(v85,cavalry-1), property(v89,'12th'-50), rank(v89,12)]).Figure 6: Semantic interpretations found by MARIE-3 for the inferred captions of Figure 1.
In general, MARIE-2 (MARIE-3 is not finished) took a median CPU time of 9.7 seconds (and a geometric mean of 10.2 seconds, the antilogarithm of the mean of the logarithms) to parse randomly selected NAWC-WD caption sentences, sentences averaging 7.4 words in length, with more difficulty for a few sentences with rare syntactic constructs. This processing used 226K of source code and 6832K of data (including 1894K of word sense statistics and 1717K of binary statistics). MARIE-2 found the correct interpretation on its first try for the majority of the test sentences, with a geometric mean of 1.9 tries. Figure 7 shows the value of different kinds of linguistic information to the interpretation of some representative sentences. Figure 8 shows statistics on four successive test sets on this particular technical dialect; word-frequency statistics for each set were calculated before going to the next, so "learning" only took place then. Note how the introduction of new syntactic and semantic rules is declining, although significant numbers of new words are still being introduced in this open-ended real-world dialect.
|1.||pacific ranges and facilities department, sled tracks.|
|2.||airms, pointer and stabilization subsystem characteristics.|
|3.||vacuum chamber in operation in laser damage facility.|
|4.||early fleet training aid: sidewinder 1 guidance section cutaway.|
|5.||awaiting restoration: explorer satellite model at artifact storage facility.|
|6.||fae i (cbu-72), one of china lake's family of fuel-air explosive weapons.|
|7.||wide-band radar signature testing of a submarine communications mast in the|
|bistatic anechoic chamber.|
|8.||the illuminating antenna is located low on the vertical tower structure|
|and the receiving antenna is located near the top.|
|Sentence||Training||Training||Final||Final||No binary||No binary||No unary||No unary|
|set 1||set 2||set 3||set 4|
|Number of new captions||217||108||172||119|
|Number of new sentences||444||219||218||128|
|Number of total words in new captions||4488||1774||1535||1085|
|Number of distinct words in new captions||939||900||677||656|
|Number of new lexicon entries required||c.150||106||139||53|
|Number of new word senses used||929||728||480||416|
|Number of new sense pairs used||1860||1527||1072||795|
|Number of lexical-processing changes required||c.30||11||8||7|
|Number of syntactic-rule changes or additions||35||41||29||10|
|Number of case-definition changes or additions||57||30||16||3|
|Number of semantic-rule changes or additions||72||57||26||14|
The caption's meaning representation can be indexed in a database under the picture name. The same linguistic processing methods can interpret natural-language queries, look up word senses in the index, and do a subgraph-graph isomorphism match between the query semantic network and the caption semantic network (Rowe, 1996; Guglielmo and Rowe, 1996) to prove that the query is covered by the caption.
While captions are generally simpler to analyze than unrestricted natural language, captioned images are not much easier than most images: They are the most valuable when they show much variety. Linguistic processing also takes much less time than image processing; NAWC-WD captions average 22 words long while their pictures need 10,000 pixels for minimal representation. Nonetheless, image processing of captioned images can provide valuable information not in captions. Captions rarely describe the size, orientation, or contrast of important objects in the image. They rarely describe easy-to-see features like whether the image is a daytime view, an outdoor view, or an historical photograph. Nor do they mention things obvious to people familiar with the picture subject, a serious problem for specialized technical images. For instance, captions rarely mention that sky or ground is shown in a picture, and NAWC-WD captions rarely mention that the photographs were taken at NAWC-WD.
MARIE's basic image processing segments the image into regions,
computes region properties, and broadly classifies regions. It uses a
robust "split-and-merge" method on thumbnail reductions (to about
10,000 pixels each) of the images. The image is split into small
irregular regions based on color similarity, and the regions are
merged in a best-first way using color and texture similarity until
the number and size of the remaining regions simultaneously achieves
several criteria. Typically this was when 50-100 regions remained.
Then some final splitting criteria are applied, and 17 key properties
of the regions are calculated. The 17 were developed from extensive
survey of a variety of captioned images. They represent key
dimensions for distinguising regions, including color, texture,
density, horizontality and verticality, boundary shape, and boundary
strength. Figure 9 shows the region segmentations found for the
Figure 1 photographs, and Figure 10 lists the properties computed for
Figure 9: Segmentations found for the images in Figure 1.
|narrowness||height/width of bounding rectangle|
|marginality||1/(1+(circumference/number border cells))|
|redness||average red brightness|
|greenness||average green brightness|
|blueness||average blue brightness|
|pixel texture||average brightness variation of adjacent cells|
|brightness trend||brightness correlation with x or y|
|symmetry||skew of center of mass|
|from bounding-rectangle center|
|contrast||average strength of the region edge|
|segments||smoothed-boundary number of inflection points|
|rectangularity||smoothed-boundary number of right angles|
|size||area in pixels|
|density||density of pixels in bounding rectangle|
|height||y-skew (unsigned) of center of mass within image|
Figure 10: Properties computed for image regions.
Reliable identification of objects in unrestricted photographs is very difficult because of the wide range of subjects and photographic conditions. Nonetheless, some experiments on the easier task of trying to classify the regions into one of 25 general categories (Rowe and Frew, 1997) provide hope of helping in matching to the caption (though the next section reports a better approach). These experiments used a random sample of 128 photographs from the NAWC-WD library covering a wide variety of subjects and activities and taken under a variety of lighting conditions. A neural network was trained to classify each region of the picture as one of 25: Airplane, airplane part, animal, bomb, bomb part, building, building part, equipment, fire, flower, helicopter, helicopter part, missile, missile part, mountain, pavement, person, person body part, rock, ship part, sky, tank, terrain, wall, and water. (Some of these are domain-dependent but easily generalizable.) 25 classes appear sufficient for many applications because captions usually refine the classifications: If the caption mentions a B-747 and no other airplanes, that's likely the airplane shape in the image. The neural network takes the 17 region properties as inputs, and computes the likelihood the region is of a particular class for each of the 25 classes. Weights connecting inputs and outputs are learned. So the output for "equipment" exploits a high weight on the number of right angles of the region border, and the output for "sky" exploits a low weight on the brightness variation of adjacent cells.
With just this simple approach without relationship constraints, 33% precision was obtained in classifying the regions in a random sample. Precision was improved to 50% with addition of another level of neural reasoning that used the linguistic-focus ideas explained in the next section, showing that caption information helps in image analysis. Still better performance could be achieved with appropriate domain-dependent region-relationship constraints (like that sky is always above terrain), but domain independence is desirable for portability to other image libraries. (The classic alternative of case-based reasoning for region classification only obtained 30% precision for 25 regions; apparently some classes show too much variation in appearance.) Image processing averaged an hour per image. But again, this time is expended only during database setup when time is not critical.
So far caption and image analysis have been considered separately, but their results must eventually be integrated. This requires finding the "linguistic focus" of the caption and "visual focus" of the image because these are implicitly cross-referenced. Study (Rowe, 1994) showed that captions are a dialect restricted in semantics as well as syntax. The headwords (usually, syntactic subjects) of caption sentences usually correspond to the most important fully-visible object(s) in the image (not necessarily the largest). In conjunctions and multi-sentence captions, each headword corresponds to an important visible object. So a pod, pylon, and bracket should be clearly visible in the image for "Radar pod and pylon; front mounting bracket". Physical-action verbs are also depicted in images when appearing as gerunds, as "loading" in "Aircraft loading by crane", as participles, as past tense, or as present tense. Verbs generally correspond to relationships in the image rather than regions.
Other nouns or verbs in a caption are generally visible in part if they are related syntactically to a headword. So "Containers on truck" implies all the containers are depicted and part but not necessarily all of the truck, while "Truck with containers" implies the opposite. Similarly, "Aircraft cockpit" guarantees only part of the aircraft is visible, since "aircraft" is an adjective here. The same is true for direct objects of physical-action verbs, like "resistor" in "Technician soldering resistor".
Captions also have several additional conventional forms that signal depiction, like "The picture shows X", "You can see X", and "X with Y" where "with" acts like a conjunction. Also, forms with "of" like "closeup of X" make X the true headword. This latter is a case of a general principle, that an undepictable headword refers its headword designation to its syntactic object. Depictability means whether a word sense is a physical object in the Wordnet concept hierarchy.
Figure 11 shows the concepts inferred by these and similar rules to be shown in the photographs of Figure 1. In tests with random photograph captions, 80% precision was obtained with 62% recall in identifying concepts shown in the photographs from the captions alone.
image4: [enlist-103], [singular] image4: ['George'-0,'Staff Sergeant'-0,serviceman-2],  image4: ['James'-0,'Major General'-0,'Taylor'-0,commander-2], [singular]Figure 11: Results of linguistic focus and depiction analysis, showing the terms (with any quantifications) inferred to apply to the subjects of the example photographs (the shaded areas in Figure 9).
image1: [gunnery-1],  image1: [shoot-109], [plural] image1: ['M1A1'-0], [plural]
Another phenomenon is that some captions are "supercaptions" that refer to more than one picture, using typically-parallel syntactic constructs. For example, in "Sled test: Pretest assembly, close-up of building, parachute deployment, and post-test damage", "sled test" maps to a set of four pictures, but the four contradictory conjuncts map to each of the successive pictures. Negative depictability is also inferable when a caption does not mention something expected for its domain. For instance, NAWC-WD tests equipment for aircraft, so a test not mentioned in a caption is guaranteed not to be shown in its photograph.
The linguistic focus of a caption corresponds to a subject or "visual focus" of its corresponding image. Captioned photographs alone have special visual semantics, since they are usually selected because they depict their subjects well. From study of sample photographs, it was observed that the visual foci of captioned images were region sets with generally five characteristics:
1. They are large;A trainable neuron was used to summarize the five factors. The best candidate region set can be found by a best-first search over region sets. This set is then matched to the linguistic focus, excluding redundant terms and those for objects too small compared to the others, like "pilot" when "aircraft" is also in linguistic focus. This requires inheritable average sizes of objects with standard deviations of their logarithms.
2. Their center of gravity is near the picture center;
3. They do not touch the edges of the photograph;
4. Their boundary has good contrast;
5. They are maximally different from non-focus regions of the picture.
Performance of this approach on a random set of images (different from those tested for image processing) was 64% precision for 40% recall. These figures were computed as ratios of numbers of pixels, so in other words 64% of the pixels selected as belonging to subjects of the picture were actually part of the subjects. Precision is the challenge since 100% recall is easy by just designating the entire picture as the subject. Segmentation was critical for these results since since only 1% precision was obtained by selecting all pixels whose color was significantly different than the color of any picture-boundary pixel. Altogether, caption-image reference analysis required 29K of source code and less than a second of CPU time per test caption. The shaded areas in Figure 9 represent the best hypotheses found for the subjects of the Figure 1 photographs. The program mistakenly selected a sign in the first picture that was close to the center of the image, but the other regions selected are correct as are their labels (Figure 11). Region classification in the manner of the last section helps avoid such mistakes, but it helps less than the five principles above.
Let us now consider what happens once a set of images has been indexed by their caption and image concepts. Such stored information can be queried by parsed English queries, or also by keyphrases, in the MARIE systems. Execution of such queries on a single computer processor can be thought of as sequential "information filtering": Successively rule out images on various criteria. Terms in a parse interpretation, or keyphrases extracted from them, can each correspond to a separate filter; but there can be many other kinds of useful filters. Efficiency is important in implementing these filters because speed at query time is critical to user satisfaction. So efficient query execution strategies must be found.
In a sequence of information filters, it often helps to put the toughest filters first, to reduce workload fastest (though filter sequences are conjunctive and give the same results in any order). This is desirable when filters need a constant amount of time per data item, as in hash lookups from a sparse hash table, but not always otherwise. (Rowe, 1996) showed the criterion for local optimality with respect to interchange of filters |i| and |i+1| in a filter sequence is:
c sub i / ( 1 - p ( f sub i | g sub i-1 ) ) <= c sub i+1 / ( 1 - p ( f sub i+1 | g sub i-1 ) )Here |f sub i| is the event of a data item passing filter |i|, |g sub i-1| is the event of a data item passing filters 1 through |i-1|, |c sub i| is the average execution cost per data item of filter |i|, and |p| means "probability". This is only a local optimality condition since |g sub i-1| represents context, but can be used heuristically to sort the filter sequence, and experiments showed that such sorting nearly always gave the globally optimal sequence. The criterion is especially valuable for placing information filters that do complex processing. An example is the subgraph-isomorphism check mentioned at the end of section 3: Such matching is valuable but time-consuming, and (Rowe, 1996) proved it should be done last among MARIE filters.
Another way to improve the efficiency of a filter sequence is to introduce appropriate redundant information filters. Redundant filters can actually help when they are faster than the filters which make them redundant. For instance, the subgraph-isomorphism filter makes redundant the filters that only match noun senses between query and caption, but the latter can quickly cut the former's workload considerably. Another redundant filter used by MARIE-2 broadly classifies the query (for instance, into "test photo", "public relations photo", and "portrait" classes), and rules out matches with incompatible caption classes; this is also much faster but redundant with respect to the subgraph-isomorphism filter. A proven sufficient criterion for local optimality with respect to nondeletion for a redundant filter |i| in a filter sequence is:
c sub i / ( 1 - p ( f sub i | g sub i-1 ) ) <= c sub i+1with the same notation as above. Again, experiments showed this led almost always to the globally optimal sequence. Other useful analytic criteria for optimality of information filtering were shown including those for optimality of disjunctive sequences, negations, and boolean combinations of filters. Using such optimizations, MARIE-1 took about two seconds per query (the geometric mean of CPU time) for a sample of queries generated by actual NAWC-WD users (which were shorter and simpler than captions). MARIE-2 took about three seconds per query, but gave better answers. Optimization took just a few seconds of CPU time and 23K of source code (Rowe, 1996).
Another way to speed up information filtering is by data parallelism, different processors trying different image sets to match to the query. (Other forms of parallelism do not help information filters much.) Load imbalances can occur when some processors finish early on their allocation of work because of random fluctuations in either processing time or the success rate of a filter. But data can be assigned randomly to processors and the load imbalance estimated quite accurately at each step, which permits judging when the cost of rebalancing the processors is justified. Such parallelism would help applications requiring high-speed retrieval like real-time robots.
The success of text information retrieval for the Internet has obscured the considerably greater difficulty of multimedia information retrieval. Naive methods like searching for keywords likely to be associated with a particular kind of image encounter low success rates. The MARIE project has explored an integrated solution to multimedia retrieval using knowledge-based methods and clues from linguistics, image analysis, presentation layout, and mathematical analysis. While perfection is not possible for information retrieval tasks, major improvements over the 1% success rate of naive keyword lookup are definitely possible with the ideas presented here. Clear synergism was obtained by using multimodal clues, confirming advantages of multimodal processing (Maybury, 1997). At the same time, MARIE uses mostly domain-independent methods that are relatively easy to extend to new image libraries. The insights from MARIE may prove important in tapping the wealth of multimedia data available on the information superhighway.
Charniak, E. (1993). Statistical Language Learning. Cambridge, MA: MIT Press.
Flickner, M., Sawhney, H. Niblack, W., Ashley, J., Huang, Q., Dom, B., Gorkani, M., Hafner, J., Lee, D., Petkovic, D., Steele, D., and Yanker, P. (1995). Query by image and video content: The QBIC System. Computer, 28 (9) (September), 23-32.
Guglielmo, E. J. and Rowe, N. C. (1996). Natural-language retrieval of images based on descriptive captions. ACM Transactions on Information Systems, 14 (3) (July), 237-267.
Hauptman, G. and Witbrock, M. (1997). Informedia: News-on-Demand Multimedia Information Acquisition and Retrieval. In Intelligent Multimedia Information Retrieval, Maybury, M. ed. Palo Alto, CA: AAAI Press, 215-239.
Maybury, M., ed. (1997). Intelligent Multimedia Information Retrieval. Palo Alto, CA: AAAI Press.
Miller, G., Beckwith, R., Fellbaum, C., Gross, D., and Miller, K. (1990). Five papers on Wordnet. International Journal of Lexicography, 3 (4) (Winter).
Ogle, V. E. (1995) Chabot: Retrieval from a relational database of images. Computer, 28 (9) (September), 40-48.
Rowe, N. C. (1994). Inferring depictions in natural-language captions for efficient access to picture data. Information Processing and Management, 30 (3), 379-388.
Rowe, N. C. (1996). Using local optimality criteria for efficient information retrieval with redundant information filters. ACM Transactions on Information Systems, 14 (2) (April), 138-174.
Rowe, N. C. and Frew, B. (1997). Automatic classification of objects in captioned depictive photographs for retrieval. Chapter 4 in Intelligent Multimedia Information Retrieval, ed. M. Maybury. Palo Alto, CA: AAAI Press, 65-79.
Rowe, N. C. and Frew, B. (1998), Automatic caption localization for photographs on World Wide Web pages. Information Processing and Management, 34 (2), 95-107.
Rowe, N. C. and Laitinen, K. (1995). Semiautomatic disabbreviation of technical text. Information Processing and Management, 31 (6), 851-857.
Smith, J. and Chang, S-F. (1996). VisualSEEk: A fully automated content-based image query system. Proceedings of ACM Multimedia 96.
Smoliar, S. and Zhang, H. (1994). Content-based video indexing and retrieval. IEEE Multimedia, Summer, 62-72.
Srihari, R. K. (1995). Automatic indexing and content-based retrieval of captioned images. IEEE Computer, 28 (9) (September), 49-56.
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