TY - GEN
T1 - Animals on the web
AU - Berg, Tamara L.
AU - Forsyth, David A.
N1 - Copyright:
Copyright 2011 Elsevier B.V., All rights reserved.
PY - 2006
Y1 - 2006
N2 - We demonstrate a method for identifying images containing categories of animals. The images we classify depict animals in a wide range of aspects, configurations and appearances. In addition, the images typically portray multiple species that differ in appearance (e.g. ukari's, vervet monkeys, spider monkeys, rhesus monkeys, etc.). Our method is accurate despite this variation and relies on four simple cues: text, color, shape and texture. Visual cues are evaluated by a voting method that compares local image phenomena with a number of visual exemplars for the category. The visual exemplars are obtained using a clustering method applied to text on web pages. The only supervision required involves identifying which clusters of exemplars refer to which sense of a term (for example, "monkey" can refer to an animal or a bandmember). Because our method is applied to web pages with free text, the word cue is extremely noisy. We show unequivocal evidence that visual information improves performance for our task. Our method allows us to produce large, accurate and challenging visual datasets mostly automatically.
AB - We demonstrate a method for identifying images containing categories of animals. The images we classify depict animals in a wide range of aspects, configurations and appearances. In addition, the images typically portray multiple species that differ in appearance (e.g. ukari's, vervet monkeys, spider monkeys, rhesus monkeys, etc.). Our method is accurate despite this variation and relies on four simple cues: text, color, shape and texture. Visual cues are evaluated by a voting method that compares local image phenomena with a number of visual exemplars for the category. The visual exemplars are obtained using a clustering method applied to text on web pages. The only supervision required involves identifying which clusters of exemplars refer to which sense of a term (for example, "monkey" can refer to an animal or a bandmember). Because our method is applied to web pages with free text, the word cue is extremely noisy. We show unequivocal evidence that visual information improves performance for our task. Our method allows us to produce large, accurate and challenging visual datasets mostly automatically.
UR - http://www.scopus.com/inward/record.url?scp=33845595951&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33845595951&partnerID=8YFLogxK
U2 - 10.1109/CVPR.2006.57
DO - 10.1109/CVPR.2006.57
M3 - Conference contribution
AN - SCOPUS:33845595951
SN - 0769525970
SN - 9780769525976
T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
SP - 1463
EP - 1470
BT - Proceedings - 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006
T2 - 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006
Y2 - 17 June 2006 through 22 June 2006
ER -