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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.... (Update)
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BibTeX entry: (Update)
R. Fergus, P. Perona, and A. Zisserman. Object class recognition by unsupervised scale-invariant learning. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2003. http://citeseer.ist.psu.edu/fergus03object.html More
@misc{ fergus03object,
author = "R. Fergus and P. Perona and A. Zisserman",
title = "Object class recognition by unsupervised scale-invariant learning",
text = "R. Fergus, P. Perona, and A. Zisserman. Object class recognition by unsupervised
scale-invariant learning. In Proc. IEEE Conf. Computer Vision and Pattern
Recognition, 2003.",
year = "2003",
url = "citeseer.ist.psu.edu/fergus03object.html" }
Citations (may not include all citations):2528 Maximum likelihood from incomplete data via the em algorithm (context) - Dempster, Laird et al. - 1976
335 Neural network-based face detection - Rowley, Baluja et al. - 1998
244 Example-based learning for view-based human face detection - Sung, Poggio - 1998
145 Rapid object detection using a boosted cascade of simple fea.. - Viola, Jones - 2001
116 Kluwer Academic Publishers (context) - Lowe, Visual - 1985
89 Feature detection with automatic scale selection - Lindeberg - 1998 ACM
84 object detection applied to faces and cars (context) - Schneiderman, Kanade et al. - 2000
73 Object Recognition by Computer (context) - Grimson - 1990 ACM
58 Unsupervised learning of models for recognition - Weber, Welling et al. - 2000 ACM DBLP
46 Indexing based on scale invariant interest points - Mikolajczyk, Schmid - 2001 DBLP
33 A probabilistic approach to object recognition using local p.. - Burl, Weber et al. - 1998
33 Learning a sparse representation for object detection - Agarwal, Roth - 2002 ACM DBLP
29 Planar object recognition using projective shape representat.. - Rothwell, Zisserman et al. - 1995
29 Constructing models for content-based image retrieval - Schmid - 2001
19 A computational model for visual selection - Amir, Geman - 1999 ACM DBLP
18 top-down segmentation (context) - Borenstein, Ullman - 2002
12 Towards automatic discovery of object categories - Weber, Welling et al. - 2000 DBLP
10 saliency and image description (context) - Kadir, Brady - 2001
6 Unsupervised Learning of Models for Object Recognition (context) - Weber - 2000
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