Deep High-order Asymmetric Supervised Hashing for Image Retrieval

Abstract

Deep hashing has recently been attracting more and more attentions for large-scale image retrieval task owing to its superior performance of search efficiency and less storage space requirements. Among deep hashing models, asymmetric deep hashing performs feature learning on query dataset and directly generates hash code on database images, significantly improving the retrieval performance of deep hashing models. Meanwhile, recently works also establish that high-order statistic of deep features are helpful to obtain more discriminant representations of images. Therefore, to boost the retrieval capability of deep hashing, this work tries to integrate merits of the high-order statistic module and the asymmetric deep hashing architecture, and it further proposes a novel deep high-order asymmetric supervised hashing (DHoASH) for image retrieval. More specifically, we utilize a powerful global covariance pooling module based on matrix power normalization to compute the second-order statistic features of input images, which is fluently embedded into an asymmetric hashing architecture in an end-to-end manner, leading to the generation of more discriminant binary hashing code. Experiment results on two benchmarks illuminates the effectiveness of the proposed DHoASH, which also achieves very competitive retrieval accuracy compared to the state-of-the-art methods.