On the Exploration of Joint Attribute Learning for Person Re-identification

This paper presents an algorithm for jointly learning a set of mid-level attributes from an image ensemble by locating clusters of dependent attributes. Human describable attributes are an active research topic due to their ability to transfer between domains, human understanding, and improvement to identification performance. Joint learning may allow for enhanced attribute classification when there is inherent dependency among the attributes. We propose an agglomerative clustering scheme to determine which sets of attributes should be learned jointly in order to maximize the margin of performance improvement. We evaluate the joint learning algorithm on a set of attributes for the task of person re-identification. We find that the proposed algorithm can improve classifier accuracy over both independent or fully joint attribute classification. Furthermore, the enhanced classifiers also improve performance on the person re-identification task. Our algorithm can be widely applicable to a variety of attribute-based visual recognition problems.