Kernelized cross-modal hashing for multimedia retrieval
Shoubiao Tan, Lingyu Hu, Anqi Wang-Xu, Jun Tang, Zhaohong Jia · 2016
Cross-modal hashing has received more and more attention because of its fast query speed and low storage cost. In this paper, we propose a flexible yet simple cross-modal hashing method to deal with the problem of cross-modal retrieval. The proposed method consists of two steps. In the first phase, we use a kernel canonical correlation analysis method named Anchor kernel canonical correlation analysis (AKCCA) to map data from different modalities into a common kernel space. In the second phase, we use the method named Supervised Hashing with Kernels (KSH) to learn hashing functions bit by bit. These two useful ingredients are combined seamlessly to achieve promising results. Experimental results on a benchmark dataset demonstrate that our method performs better than several state-of-the-art methods.