Structure-Preserving Video Hashing via Self-Supervised Transformer for Retrieval

Self-supervised video hashing aims at generating hash codes and performing fast video content retrieval by leveraging the visual content information inherent in the videos themselves. Most existing methods often overlook the structure-preserving information within the visual content of the videos and thus cannot learn an effective discriminative video representation. In this paper, a Structure-Preserving Video Hashing (SPVH) via a self-supervised Transformer for retrieval is proposed by exploring the global relationships, local relationships, and inter-video relationships in the visual content of videos. In the proposed SPVH, a Transformer-based autoencoder model is used to extract the deep features of the videos. Moreover, a new structure-preserving loss function with the clustering loss, constraint loss, contrastive loss, and reconstruction loss is designed to capture the structural information of the videos. Extensive experiments are conducted on two large-scale video datasets. The results demonstrate the superior performance of our SPVH compared to some state-of-the-art methods.