Learning Discriminative Hashing Codes for Cross-Modal Retrieval based on Multi-view Features
This work addresses the problem of improving retrieval accuracy in cross-modal tasks for applications like image-text search, though it is incremental as it builds on existing hashing techniques with multi-view integration.
The paper tackled the problem of limited representation capacity in single-view hashing for cross-modal retrieval by using multi-view features to enrich information and learn discriminative hash codes, achieving superior results compared to state-of-the-art methods on datasets like WiKi, MMED, MIRFlickr, and NUS-WIDE.
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.