Abstract 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.
Located Hidden Random Fields: Learning Discriminative Parts for Object Detection
Computer Vision – ECCV 2006 ; 5 ; 302-315
Lecture Notes in Computer Science ; 3953 , 5
2006-01-01
14 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Located Hidden Random Fields: Learning Discriminative Parts for Object Detection
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