Cross-Modal Hashing Retrieval Based on Density Clustering

Abstract

Cross-modal hashing retrieval methods have attracted much attention for their effectiveness and efficiency. However, most of the existing hashing methods have the problem of how to precisely learn potential correlations between different modalities from binary codes with minimal loss. In addition, solving binary codes in different modalities is an NP-hard problem. To overcome these challenges, we initially propose a novel adaptive fast cross-modal hashing retrieval method under the inspiration of DBSCAN clustering algorithm, named Cross-modal Hashing Retrieval Based on Density Clustering (DCCH). DCCH utilizes the global density correlation between different modalities to select representative instances to replace the entire data precisely. Furthermore, DCCH excludes the adverse effects of noise points and leverages the discrete optimization process to obtain hash functions. The extensive experiments show that DCCH is superior to other state-of-the-art cross-modal methods on three benchmark bimodal datasets, i.e., Wiki, MIRFlickr and NUS-WIDE. Therefore, the experimental results also prove that our method DCCH is comparatively usable and efficient.