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Object class recognition by unsupervised scale-invariant learning
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)
- OpenAlex
- W2154422044
- Cited by
- 2,023
- References
- 3
- FWCI
- 103.39
- Type
- conference paper
- Language
- en
- Open access
- closed
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