Exploiting Low-rank Structure for Discriminative Sub-categorization

In visual recognition, sub-categorization has been proposed to deal with large intraclass variance of samples in a category. Instead of learning a single classifier for each category, discriminant sub-categorization approaches divide a category into several subcategories and simultaneously train classifiers for each sub-category. In this paper, we propose a novel approach for discriminative sub-categorization. Our method jointly trains the exemplar classifier for each positive sample to address the intra-variance of a category and exploits the low rank structure to preserve common information while discovering sub-categories. We formulate the problem as a convex objective function and introduce an efficient solver based on alternating direction method of multipliers. Comprehensive experiments on various datasets demonstrate the effectiveness and efficiency of the proposed method in both sub-category discovery and visual recognition.