SOMH: A self-organizing map based topology preserving hashing method

Hashing based approximate nearest neighbor (ANN) search techniques have attracted considerable attention in media search community because of its good potential for low storage cost and fast query speed. Many hashing based ANN search methods have been proposed; but, most of them just consider to keep the similarity relationship of data points during mapping instead of topology of data. It is well known that Self-Organizing Map can keep topology structure while conducting mapping task. Motivated by this, in this paper, we propose a Self-Organizing Map based hashing framework-SOMH, which cannot only keep similarity relationship, but also preserve topology of data. Specifically, in SOMH, Self-Organizing Map is introduced to map data points into hamming space. In addition, in order to make it work well on short and long binary codes, we propose a relaxed version of SOMH and a product space SOMH, respectively. For the optimization problem of the relaxed SOMH, we also present an iterative solution. Moreover, we further propose an extended version of SOMH, which can work well on multimodal data search task, i.e., cross-modal search. To test the performance of these proposed algorithms, we conduct experiments on three data sets-SIFT1M, GIST1M and Wiki (a multimodal dataset). Experimental results show that SOMH can outperform or is comparable to several state-of-the-arts.