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DAGM
2006
Springer
13 years 11 months ago
Towards Unsupervised Discovery of Visual Categories
Recently, many approaches have been proposed for visual object category detection. They vary greatly in terms of how much supervision is needed. High performance object detection m...
Mario Fritz, Bernt Schiele
CVPR
2005
IEEE
14 years 9 months ago
A Bayesian Hierarchical Model for Learning Natural Scene Categories
We propose a novel approach to learn and recognize natural scene categories. Unlike previous work [9, 17], it does not require experts to annotate the training set. We represent t...
Fei-Fei Li 0002, Pietro Perona, California Institu...
CVPR
2009
IEEE
15 years 2 months ago
Unsupervised Learning of Hierarchical Spatial Structures In Images
The visual world demonstrates organized spatial patterns, among objects or regions in a scene, object-parts in an object, and low-level features in object-parts. These classes o...
Devi Parikh (Carnegie Mellon University), C. Lawre...
ICIP
2003
IEEE
14 years 9 months ago
Feature selection for unsupervised discovery of statistical temporal structures in video
We present algorithms for automatic feature selection for unsupervised structure discovery from video sequences. Feature selection in this scenario is hard because of the absence ...
Lexing Xie, Shih-Fu Chang, Ajay Divakaran, Huifang...
CVPR
2006
IEEE
14 years 9 months ago
Local Features, All Grown Up
We present a technique to adapt the domain of local features through the matching process to augment their discriminative power. We start with local affine features selected and n...
Andrea Vedaldi, Stefano Soatto