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Recognizing the presence of object classes in an image, or image classification, has become an increasingly important topic of interest. Equally important, however, is also the capability to locate these object classes in the image. We consider in this paper an approach to these two related problems with the primary goal of minimizing the training requirements so as to allow for ease of adding new object classes, as opposed to approaches that favor training a suite of object-specific classifiers. To this end, we provide the analysis of an exemplar-based approach that leverages unsupervised clustering for classification purpose, and sliding window matching for localization. While such exemplar based approach by itself is brittle towards intraclass and viewpoint variations, we achieve robustness by introducing a novel Conditional Random Field model that facilitates a straightforward accept/reject decision of the localized object classes. Performance of our approach on the PASCAL Visual Object Challenge 2007 dataset demonstrates its efficacy. © 2011 Springer-Verlag.

Original publication

DOI

10.1007/978-3-642-24028-7_53

Type

Journal article

Journal

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Publication Date

05/10/2011

Volume

6938 LNCS

Pages

573 - 585