Discriminative Supervised Hashing for Cross-Modal similarity Search

TL;DR

This paper introduces Discriminative Supervised Hashing (DSH), a novel framework that learns unified binary codes for cross-modal data by jointly optimizing classifier, subspace, and matrix factorization to improve retrieval accuracy.

Contribution

The paper proposes a new hashing framework that enhances discriminative power and preserves data structure for cross-modal similarity search.

Findings

DSH outperforms existing methods on three datasets.

The framework effectively preserves class-specific semantic content.

Non-linear projection improves cross-modal retrieval accuracy.

Abstract

With the advantage of low storage cost and high retrieval efficiency, hashing techniques have recently been an emerging topic in cross-modal similarity search. As multiple modal data reflect similar semantic content, many researches aim at learning unified binary codes. However, discriminative hashing features learned by these methods are not adequate. This results in lower accuracy and robustness. We propose a novel hashing learning framework which jointly performs classifier learning, subspace learning and matrix factorization to preserve class-specific semantic content, termed Discriminative Supervised Hashing (DSH), to learn the discrimative unified binary codes for multi-modal data. Besides, reducing the loss of information and preserving the non-linear structure of data, DSH non-linearly projects different modalities into the common space in which the similarity among heterogeneous data points can be measured. Extensive experiments conducted on the three publicly available datasets demonstrate that the framework proposed in this paper outperforms several state-of -the-art methods.