Recent years have witnessed the growing popularity of cross-modal hashing for fast multi-modal data retrieval. Most existing cross-modal hashing methods project heterogeneous data directly into a common space with linear projection matrices. However, such scheme will lead to large error as there will probably be some heterogeneous data with semantic similarity hard to be close in latent space when linear projection is used. In this paper, we propose a dictionary learning cross-modal hashing (DLCMH) to perform cross-modal similarity search. Instead of projecting data directly, DLCMH learns dictionaries and generates sparse representation for each instance, which is more suitable to be projected to latent space. Then, it assumes that all modalities of one instance have identical hash codes, and gets final binary codes by minimizing quantization error. Experimental results on two real-world datasets show that DLCMH outperforms or is comparable to several state-of-the-art hashing models.
['Xinshun Xu']
Dictionary Learning Based Hashing for Cross-Modal Retrieval