Learning Discriminative Hashing Codes For Cross-modal Retrieval Based On Multi-view Features

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

Stats

• citations2
• S2 citations—
• github stars0
• HF likes0
• heat score3.58
• arxiv keyyu2018learning

Related papers

• Semi-supervised Hashing For Semi-paired Cross-view Retrieval (2018)6.77
• Discriminative Supervised Hashing For Cross-modal Similarity Search (2018)7.81
• Discriminative Cross-view Binary Representation Learning (2018)4.52
• Unsupervised Multi-modal Hashing For Cross-modal Retrieval (2019)8.35
• Robust Hashing For Multi-view Data: Jointly Learning Low-rank Kernelized Similarity Consensus And Hash Functions (2016)11.19
• FDDH: Fast Discriminative Discrete Hashing For Large-scale Cross-modal Retrieval (2021)15.50
• Efficient Discrete Supervised Hashing For Large-scale Cross-modal Retrieval (2019)11.08
• Locality Preserving Multiview Graph Hashing For Large Scale Remote Sensing Image Search (2023)4.52