SDMCH: Supervised Discrete Manifold-Embedded Cross-Modal Hashing

SDMCH: Supervised Discrete Manifold-Embedded Cross-Modal Hashing

Xin Luo, Xiao-Ya Yin, Liqiang Nie, Xuemeng Song, Yongxin Wang, Xin-Shun Xu

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Main track. Pages 2518-2524. https://doi.org/10.24963/ijcai.2018/349

Cross-modal hashing methods have attracted considerable attention. Most pioneer approaches only preserve the neighborhood relationship by constructing the correlations among heterogeneous modalities. However, they neglect the fact that the high-dimensional data often exists on a low-dimensional manifold embedded in the ambient space and the relative proximity between the neighbors is also important. Although some methods leverage the manifold learning to generate the hash codes, most of them fail to explicitly explore the discriminative information in the class labels and discard the binary constraints during optimization, generating large quantization errors. To address these issues, in this paper, we present a novel cross-modal hashing method, named Supervised Discrete Manifold-Embedded Cross-Modal Hashing (SDMCH). It can not only exploit the non-linear manifold structure of data and construct the correlation among heterogeneous multiple modalities, but also fully utilize the semantic information. Moreover, the hash codes can be generated discretely by an iterative optimization algorithm, which can avoid the large quantization errors. Extensive experimental results on three benchmark datasets demonstrate that SDMCH outperforms ten state-of-the-art cross-modal hashing methods.
Keywords:
Constraints and SAT: Constraint Optimisation
Computer Vision: Recognition: Detection, Categorization, Indexing, Matching, Retrieval, Semantic Interpretation
Machine Learning Applications: Applications of Supervised Learning
Machine Learning: Dimensionality Reduction and Manifold Learning
Multidisciplinary Topics and Applications: Information Retrieval