Sub-Category Optimization for Multi-view Multi-pose Object Detection

Object category detection with large appearance variation is a fundamental problem in computer vision. The appearance of object categories can change due to intra-class variability, viewpoint, and illumination. For object categories with large appearance change a sub-categorization based approach is necessary. This paper proposes a sub-category optimization approach that automatically divides an object category into an appropriate number of sub-categories based on appearance variation. Instead of using a predefined intra-category sub-categorization based on domain knowledge or validation datasets, we divide the sample space by unsupervised clustering based on discriminative image features. Then the clustering performance is verified using a sub-category discriminant analysis. Based on the clustering performance of the unsupervised approach and sub-category discriminant analysis results we determine an optimal number of sub-categories per object category. Extensive experimental results are shown using two standard and the authors´ own databases. The comparison results show that our approach outperforms the state-of-the-art methods.