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ECCV
2006
Springer

Located Hidden Random Fields: Learning Discriminative Parts for Object Detection

10 years 3 months ago
Located Hidden Random Fields: Learning Discriminative Parts for Object Detection
This paper introduces the Located Hidden Random Field (LHRF), a conditional model for simultaneous part-based detection and segmentation of objects of a given class. Given a training set of images with segmentation masks for the object of interest, the LHRF automatically learns a set of parts that are both discriminative in terms of appearance and informative about the location of the object. By introducing the global position of the object as a latent variable, the LHRF models the long-range spatial configuration of these parts, as well as their local interactions. Experiments on benchmark datasets show that the use of discriminative parts leads to state-of-the-art detection and segmentation performance, with the additional benefit of obtaining a labeling of the object's component parts.
Ashish Kapoor, John M. Winn
Added 16 Oct 2009
Updated 16 Oct 2009
Type Conference
Year 2006
Where ECCV
Authors Ashish Kapoor, John M. Winn
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