Discriminative Visual-Semantic Collaborative Online Decomposition Hashing for Streaming Image Data Retrieval

Online hashing has gained significant interest for its tremendous promise in handling large-scale streaming image data. However, there are still several problems to be solved. Firstly, many existing methods struggle to fully utilize previous knowledge and fail to effectively mitigate catastrophic forgetting. Secondly, current online hashing methods lack targeted guidance for hash function learning, resulting in weak discriminative ability. Thirdly, many current methods adopt ineffective optimization methods to learn hash code. This paper proposes a Discriminative Visual-semantic Collaborative Online Decomposition Hashing method, abbreviated as DVsCODH. It includes two steps: hash code learning and hash function learning. In the first step, DVsCODH adopts an online pairwise similarity supervision to guide hash code learning, and simultaneously introduces a visual-semantic collaborative online decomposition strategy to adaptively fuse visual and semantic information. Through incremental online learning, DVsCODH is capable of extracting complete similarity, visual and high-level semantic information of the database. In the second step, DVsCODH leverages database-wide similarity to supervise hash function online learning. Furthermore, we propose an effective online discrete optimization algorithm that directly generate hash code, thereby enhancing model training efficiency. Experimental results on three public image datasets demonstrate the excellent retrieval performance of DVsCODH.