Deep Category-Aware Hashing for Object Retrieval in Multi-Label Image

Abstract

Hashing learning for category-aware object retrieval in multi-label image is a challenging topic, in which the user is only interested in a certain object included in the query image rather than the entire image. Thus, it aims to find images which contain object similar to the interested one. However, previous hashing methods pre-select plenty of bounding box proposals or involve multiple independent steps to generate object-level representation, which may be suboptimal. In this paper, we propose a lightweight yet effective end-to-end deep category-aware hashing(DCAH) framework, which can generate individual hash code for each object included in the image by image-level label information, of which the key point is that it can directly localize object region with the assistance of category attention map. Extensive experiments on four benchmark datasets have demonstrated that our method achieves promising improvements on category-aware object retrieval results over the state-to-the-art methods.