Cross-media hashing with Centroid Approaching
Cross-media retrieval has received increasing interest in recent years, which aims to addressing the semantic correlation issues within rich media. As two key aspects, cross-media representation and indexing have been studied for dealing with cross-media similarity measure and the scalability issue, respectively. In this paper, we propose a new cross-media hashing scheme, called Centroid Approaching Cross-Media Hashing (CAMH), to handle both cross-media representation and indexing simultaneously. Different from existing indexing methods, the proposed method introduces semantic category information into the learning procedure, leading to more exact hash codes of multiple media type instances. In addition, we present a comparative study of cross-media indexing methods under a unique evaluation framework. Extensive experiments on two commonly used datasets demonstrate the good performance in terms of search accuracy and time complexity.