Online Asymmetric Supervised Discrete Cross-Modal Hashing for Streaming Multimedia Data

Abstract

Cross-modal online hashing, which uses freshly received data to retrain the hash function gradually, has become a research hotspot as a means of handling the massive amounts of streaming data that have been brought about by the fast growth of multimedia technology and the popularity of portable devices. However, in the process of processing stream data in most methods, on the one hand, the relationship between modal classes and the common features between label vectors and binary codes is not fully explored. On the other hand, the semantic information in the old and new data modes is not fully utilized. In this post, we offer Online Asymmetric Supervised Discrete Cross-Modal Hashing for Streaming Multimedia Data (OASCH) as a solution. This study integrates the concept cognition mechanism of dynamic incremental samples and an asymmetric knowledge guidance mechanism into the online hash learning framework. The proposed algorithmic model takes into account the knowledge similarity between newly arriving data and the existing dataset, as well as the knowledge similarity within the new data itself. It projects the hash codes associated with new incoming sample data into the potential space of concept cognition. By doing so, the model maximizes the mining of implicit semantic similarities within streaming data across different time points, resulting in the generation of compact hash codes with enhanced discriminative power, we further propose an adaptive edge regression strategy. Our method surpasses several current sophisticated cross-modal hashing techniques regarding both retrieval efficiency and search accuracy, according to studies on three publicly available multimedia retrieval datasets.