Finding pictures of objects in large collections of images
Research output: Contribution to journal › Conference article › Research › peer-review
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Finding pictures of objects in large collections of images. / Forsyth, David A.; Malik, Jitendra; Fleck, Margaret M.; Greenspan, Hayit; Leung, Thomas; Belongie, Serge; Carson, Chad; Bregler, Chris.
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 1996, p. 335-360.Research output: Contribution to journal › Conference article › Research › peer-review
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TY - GEN
T1 - Finding pictures of objects in large collections of images
AU - Forsyth, David A.
AU - Malik, Jitendra
AU - Fleck, Margaret M.
AU - Greenspan, Hayit
AU - Leung, Thomas
AU - Belongie, Serge
AU - Carson, Chad
AU - Bregler, Chris
N1 - Funding Information: We would like to thank R. Blasi and K. Murphy who collaborated with S. Belongie in the work on learning decision trees for visual concept classification. We thank Joe Mundy for suggesting that the response of a grouper may indicate the presence of an object. Aspects of this research were supported by the National Science Foundation under grants IRI-9209728, IRI-9420716, IRI-9501493, an NSF Young Investigator award, an NSF Digital Library award IRI-9411334, an instrumentation award CDA-9121985, and by a Berkeley Fellowship. Publisher Copyright: © 1996, Springer Verlag. All rights reserved.
PY - 1996
Y1 - 1996
N2 - Retrieving images from very large collections, using image content as a key, is becoming an important problem. Users prefer to ask for pictures using notions of content that are strongly oriented to the presence of abstractly defined objects. Computer programs that implement these queries automatically are desirable, but are hard to build because conventional object recognition techniques from computer vision cannot recognize very general objects in very general contexts. This paper describes our approach to object recognition, which is structured around a sequence of increasingly specialized grouping activities that assemble coherent regions of image that can be shown to satisfy increasingly stringent constraints. The constraints that are satisfied provide a form of object classification in quite general contexts. This view of recognition is distinguished by: far richer involvement of early visual primitives, including color and texture; hierarchical grouping and learning strategies in the classification process; the ability to deal with rather general objects in uncontrolled configurations and contexts. We illustrate these properties with four case-studies: one demonstrating the use of color and texture descriptors; one showing how trees can be described by fusing texture and geometric properties; one learning scenery concepts using grouped features; and one showing how this view of recognition yields a program that can tell, quite accurately, whether a picture contains naked people or not.
AB - Retrieving images from very large collections, using image content as a key, is becoming an important problem. Users prefer to ask for pictures using notions of content that are strongly oriented to the presence of abstractly defined objects. Computer programs that implement these queries automatically are desirable, but are hard to build because conventional object recognition techniques from computer vision cannot recognize very general objects in very general contexts. This paper describes our approach to object recognition, which is structured around a sequence of increasingly specialized grouping activities that assemble coherent regions of image that can be shown to satisfy increasingly stringent constraints. The constraints that are satisfied provide a form of object classification in quite general contexts. This view of recognition is distinguished by: far richer involvement of early visual primitives, including color and texture; hierarchical grouping and learning strategies in the classification process; the ability to deal with rather general objects in uncontrolled configurations and contexts. We illustrate these properties with four case-studies: one demonstrating the use of color and texture descriptors; one showing how trees can be described by fusing texture and geometric properties; one learning scenery concepts using grouped features; and one showing how this view of recognition yields a program that can tell, quite accurately, whether a picture contains naked people or not.
UR - http://www.scopus.com/inward/record.url?scp=84979074103&partnerID=8YFLogxK
U2 - 10.1007/3-540-61750-7_36
DO - 10.1007/3-540-61750-7_36
M3 - Conference article
AN - SCOPUS:84979074103
SP - 335
EP - 360
JO - Lecture Notes in Computer Science
JF - Lecture Notes in Computer Science
SN - 0302-9743
T2 - International Workshop on Object Representation in Computer Vision II, ECCV 1996
Y2 - 13 April 1996 through 14 April 1996
ER -
ID: 302163653