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Cited by 2,023

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

Object class recognition by unsupervised scale-invariant learning

Fergus, R., Perona, P., Zisserman, A.·2003·2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings.
DOI 10.1109/cvpr.2003.1211479
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Abstract

We present a method to learn and recognize object class models from unlabeled and unsegmented cluttered scenes in a scale invariant manner. Objects are modeled as flexible constellations of parts. A probabilistic representation is used for all aspects of the object: shape, appearance, occlusion and relative scale. An entropy-based feature detector is used to select regions and their scale within the image. In learning the parameters of the scale-invariant object model are estimated. This is done using expectation-maximization in a maximum-likelihood setting. In recognition, this model is used in a Bayesian manner to classify images. The flexible nature of the model is demonstrated by excellent results over a range of datasets including geometrically constrained classes (e.g. faces, cars) and flexible objects (such as animals).

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Metadata

Authors
Fergus, R., Perona, P., & Zisserman, A.
Venue
2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings.
Publisher
IEEE Comput. Soc
Published
2003-11-20
Volume
2
Pages
II-264-II-271
ISSN
1063-6919 (ISSN-L)
DOI
10.1109/cvpr.2003.1211479
OpenAlex
W2154422044
Cited by
2,023
References
3
FWCI
103.39
Type
conference paper
Language
en
Open access
closed

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