A New Ranking based Semantic Hashing Method for Deep Image Retrieval

Visual search in millions of samples in high-dimensional feature space is computationally expensive and challenging. A natural solution is to reduce the dimension of image representation by mapping each sample into compact binary code. In this paper, we propose a Ranking Based Semantic Hashing (RBSH) method to tackle with this problem. Observing that semantic structures carry complementary information, this paper takes advantage of semantic supervision for training high quality hashing, the semantic mapping between the high-dimensional feature space of samples and the reduced representation space of binary code, with the help of pre-trained word2vec. Specifically, the proposed method learns the mapping based on two criteria: the contrastive ranking loss and the orthogonality constraint. The former preserves the ordering of relative similarity in image pairs, while the latter makes different bit in the hash stream as orthogonal as possible. Extensive experimental study has been conducted on VOC2012 and ILSVRC2014 image sets, demonstrating that the proposed approach generally outperforms the state-of-the-art hashing techniques based methods in image search.