OLCH: Online Label Consistent Hashing for streaming cross-modal retrieval

Cross-modal hashing has received growing interest to facilitating efficient retrieval across large-scale multi-modal data, and existing methods still face three challenges: 1) Most offline learning works are unsuitable for processing and training with streaming multi-modal data. 2) Current online learning methods rarely consider the potential interdependency between the label categories. 3) Existing supervised methods often utilize pairwise label similarities or adopt relaxation scheme to learn hash codes, which, respectively, require much computation time or accumulate large quantization loss during the learning process. To alleviate these challenges, this paper presents an efficient Online Label Consistent Hashing (OLCH) approach for streaming cross-modal retrieval. The proposed approach first exploits the relative similarity of semantic labels and utilizes the multi-class classification to derive the common semantic vector. Then, an online semantic representation learning framework is adaptively designed to preserve the semantic similarity across different modalities, and a mini-batch online gradient descent approach associated with forward–backward splitting is developed to discriminatively optimize the hash functions. Accordingly, the hash codes are incrementally learned with high discriminative capability, while avoiding high computation complexity to process the streaming data. Extensive experiments highlight the superiority of the proposed approach and show its very competitive performance in comparison with the state-of-the-arts.