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.