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Decomposition, discovery and detection of visual categories using topic models
2008
2008 IEEE Conference on Computer Vision and Pattern Recognition
We present a novel method for the discovery and detection of visual object categories based on decompositions using topic models. The approach is capable of learning a compact and low dimensional representation for multiple visual categories from multiple view points without labeling of the training instances. The learnt object components range from local structures over line segments to global silhouette-like descriptions. This representation can be used to discover object categories in a
doi:10.1109/cvpr.2008.4587803
dblp:conf/cvpr/FritzS08
fatcat:z2w22jyxezhwxd4fwmozi2u2xy