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Cited by 265

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

A Sparse Object Category Model for Efficient Learning and Exhaustive Recognition

Fergus, R., Perona, P., Zisserman, A.·2005·2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)

Abstract

We present a "parts and structure" model for object category recognition that can be learnt efficiently and in a semi-supervised manner: the model is learnt from example images containing category instances, without requiring segmentation from background clutter. The model is a sparse representation of the object, and consists of a star topology configuration of parts modeling the output of a variety of feature detectors. The optimal choice of feature types (whose repertoire includes interest points, curves and regions) is made automatically. In recognition, the model may be applied efficiently in an exhaustive manner, bypassing the need for feature detectors, to give the globally optimal match within a query image. The approach is demonstrated on a wide variety of categories, and delivers both successful classification and localization of the object within the image.

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Crossref · 10.1109/cvpr.2005.47

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Metadata

Authors
Fergus, R., Perona, P., & Zisserman, A.
Venue
2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)
Publisher
IEEE
Published
2005-07-27
Volume
1
Pages
380-387
ISSN
1063-6919 (ISSN-L)
OpenAlex
W2105488551
Cited by
265
References
3
FWCI
26.31
Type
conference paper
Language
en
Open access
closed

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