Hypergraph Spectral Hashing for image retrieval with heterogeneous social contexts

The development of social media brings great challenges to image retrieval on both efficiency and accuracy. In addition to achieving fast similarity search over large scale data, it is very crucial to represent the complex and high-order relationships among the social contents to improve the semantic understanding of social images.In this paper, unified hypergraph is implemented to model the various relationships among images and other contexts in social media. Then we extend traditional spectral hashing to hypergraph to accelerate similarity search of social images by mapping semantically related vertices into similar binary codes within a short Hamming distance. In addition, out-of-sample extension is implemented in a supervised manner and different strategies of fusing different social contexts are compared and discussed in this work. We evaluated our approach on the dataset crawled from Flickr and the experiment results indicate that our proposed HSH approach is both efficient and effective.