Description: 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 automati-
cally learns a set of parts that are both discriminative in terms of appear-
ance 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 con guration 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 bene t of obtaining a labeling of the
object s component parts.
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