PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Shape-Based Object Localization for Descriptive Classification
Geremy Heitz, Gal Elidan, Ben Packer and Daphne Koller
International Journal of Computer Vision Volume 84, Number 1, pp. 40-62, 2009.


Discriminative tasks, including object categorization and detection, are central components of high-level computer vision. However, sometimes we are interested in a finer-grained characterization of the object’s properties, such as its pose or articulation. In this paper we develop a probabilisticmethod (LOOPS) that can learn a shape and appearance model for a particular object class, and be used to consistently localize constituent elements (landmarks) of the object’s outline in test images. This localization effectively projects the test image into an alternative representational space that makes it particularly easy to perform various descriptive tasks. We apply our method to a range of object classes in cluttered images and demonstrate its effectiveness in localizing objects and performing descriptive classification, descriptive ranking, and descriptive clustering.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Learning/Statistics & Optimisation
ID Code:7061
Deposited By:Gal Elidan
Deposited On:25 February 2011