Boosting relative spaces for categorizing objects with large intra-class variation

Yi Ouyang, Ming Tang, Jinqiao Wang, Hanqing Lu, Songde Ma

Abstract: In this paper, a novel method for object categorization is proposed. We first analyze the phenomenon of large intra-class variation and attribute it to the "subcategory" problem. To reveal the local and distinct properties of the different subcategories, relative spaces are constructed. Then the weighted FLDs (Fisher Linear Discriminant) as weak learners trained in relative spaces are integrated with the boosting framework to form the final classifier. Experiments on 8 categories from Caltech database show the effectiveness of our algorithm.