Proxy-Based Graph Convolutional Hashing for Cross-Modal Retrieval

Abstract

Cross-modal hashing retrieval approaches have received extensive attention owing to their storage superiority and retrieval efficiency. To achieve better retrieval performances, hashing methods seek to embed more semantic information of multi-modal data into hash codes. Existing deep cross-modal hashing methods typically learn hash functions from the similarity of paired data to generate hash codes. However, such locally-oriented learning methods often suffer from low efficiency and incomplete acquisition of semantic information. To address these challenges, this paper presents a novel deep hashing approach, called Proxy-based Graph Convolutional Hashing (PGCH), for cross-modal retrieval. Specifically, we use global similarity to construct proxy hash codes for two different modalities. This strategy of these proxy hash codes ensures that they include data points with significant distribution differences. It helps to match data from different modalities to different proxy hash codes, which can capture the global similarity of multi-modal hash codes and improve the efficiency of hash code learning. Subsequently, we employ a multi-modal contrastive loss to learn the global similarity. Furthermore, by constructing a proxy hash matrix from the proxy hash codes, we apply graph convolution to efficiently narrow the gap between different modalities, leading to a substantial improvement in retrieval performance for cross-modal retrieval tasks. The comprehensive experiments on four benchmark multimedia datasets demonstrate that our PGCH approach achieves better retrieval performances than a bundle of state-of-the-art hashing approaches.