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Abstract: We propose an unsupervised, probabilistic method for learning visual feature hierarchies. Starting from local, low-level features computed at interest point locations, the method combines these primitives into high-level abstractions. (Update)
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2.1: Statistical Learning of Visual Feature Hierarchies - Fabien Scalzo And (2005) (Correct)
0.3: Visual Feature Learning - Piater (2001) (Correct)
0.2: Unsupervised Learning of Dense Hierarchical Appearance.. - Scalzo, Piater (Correct)
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0.0: Unknown - (Correct)
BibTeX entry: (Update)
@misc{ and-unsupervised,
author = "Fabien Scalzo And",
title = "Unsupervised Learning of Visual Feature Hierarchies",
url = "citeseer.ist.psu.edu/761169.html" }
Citations (may not include all citations):1543 Probabilistic reasoning in intelligent systems: Networks of .. (context) - Pearl - 1988
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50 Object class recognition by unsupervised scaleinvariant lear.. - Perona, Fergus et al. - 2003
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35 Hierarchical models of object recognition in cortex - Riesenhuber, Poggio - 1999
33 Learning a sparse representation for object detection - Agarwal, Roth - 2002
21 Nonparametric belief propagation - Sudderth, Ihler et al. - 2003
19 A performance evaluation of local descriptors - Mikolajczyk, Schmid - 2003
19 A Bayesian approach to unsupervised one-shot learning of obj.. (context) - Fei-Fei, Fergus et al. - 2003
5 Unsupervised learning of combination features for hierarchic.. - Wersing, Koerner - 2002
4 Distinctive features should be learned - Piater, Grupen - 2000
2 Differences in the coding of spatial relations in face ident.. (context) - Cooper, Wojan - 2000
2 A hierarchical axis of object processing stages in the human.. (context) - Lerner, Hendler et al. - 2001
2 Object recognition with many local features (context) - Helmer, Lowe - 2004
Documents on the same site (http://www.montefiore.ulg.ac.be/~scalzo/):
Unsupervised Learning of Dense Hierarchical Appearance.. - Scalzo, Piater (Correct)
Statistical Learning of Visual Feature Hierarchies - Fabien Scalzo And (2005) (Correct)
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