Adaptive Asymmetric Supervised Cross-Modal Hashing with consensus matrix

Abstract

Supervised hashing has garnered considerable attention in cross-modal retrieval by programming annotated diverse modality data into the unified binary representation that facilitates efficient retrieval and lightweight storage. Despite its advantages, a major challenge remains, how to get the utmost out of annotated information and derive robust common representation that accurately preserves the intrinsic relations across heterogeneous modalities. In this paper, we present an innovative Adaptive Asymmetric Supervised Cross-modal Hashing method with consensus matrix to tackle the problem. We begin by formulating the proposition through matrix factorization to obtain the common representation utilizing consensus matrix efficiently. To safeguard the completeness of diverse modality data, we incorporate them via adaptive weight factors along with nuclear norms. Furthermore, an asymmetric hash learning framework between the representative coefficient matrices that come from common representation and semantic labels was constructed to constitute concentrated hash codes. Additionally, a valid discrete optimization algorithm was programmed. Comprehensive experiments conducted on MIRFlirck, NUS-WIDE, and IARP-TC12 datasets validate that A2SCH outperforms leading-edge hashing methods in cross-modal retrieval tasks.