Learning Discriminative Hashing Codes for Cross-Modal Retrieval based on Multi-view Features

TL;DR

This paper introduces a multi-view hashing framework that leverages complementary features from images and texts to generate discriminative codes, significantly improving cross-modal retrieval performance.

Contribution

The proposed method uniquely combines multi-view feature fusion with a joint classifier and subspace learning framework for more effective hashing.

Findings

Outperforms state-of-the-art methods on multiple datasets

Effectively fuses multi-view features for richer representations

Achieves superior retrieval accuracy in cross-modal tasks

Abstract

Hashing techniques have been applied broadly in retrieval tasks due to their low storage requirements and high speed of processing. Many hashing methods based on a single view have been extensively studied for information retrieval. However, the representation capacity of a single view is insufficient and some discriminative information is not captured, which results in limited improvement. In this paper, we employ multiple views to represent images and texts for enriching the feature information. Our framework exploits the complementary information among multiple views to better learn the discriminative compact hash codes. A discrete hashing learning framework that jointly performs classifier learning and subspace learning is proposed to complete multiple search tasks simultaneously. Our framework includes two stages, namely a kernelization process and a quantization process. Kernelization aims to find a common subspace where multi-view features can be fused. The quantization stage is designed to learn discriminative unified hashing codes. Extensive experiments are performed on single-label datasets (WiKi and MMED) and multi-label datasets (MIRFlickr and NUS-WIDE) and the experimental results indicate the superiority of our method compared with the state-of-the-art methods.