Rank-Consistency Multi-Label Deep Hashing

In this paper, we present a deep hashing method for multi-label image retrieval, which uses a rank list to provide global supervision information. Unlike most existing approaches using shallow models to learn hash functions for multi-label images, we deepen the DNN structure to extract powerful features. In addition, we apply a rank-consistency objective function to align the similarity orders in the hamming space and the ones from the original space. Compared with conventional contrast loss and triplet loss, our listwise ranking can capture sufficient global information. We also propose a multi-label softmax cross-entropy loss to strengthen the discriminative power. Specifically, we consider the number of common labels of multi-label images as the metric of similarity in the original space with the hamming distance between binary codes as the distance metric in the hamming space. Experimental results on MIRFLICKR-25K and IAPRTC12 are presented to show the effectiveness of our proposed approach.