Semi-supervised spectral hashing for fast similarity search

Fast similarity search has been a key step in many large-scale computer vision and information retrieval tasks. Recently, there are a surge of research interests on the hashing-based techniques to allow approximate but highly efficient similarity search. Most existing hashing methods are unsupervised, which demonstrate the promising performance using the information of unlabeled data to generate binary codes. In this paper, we propose a novel semi-supervised hashing method to take into account the pairwise supervised information including must-link and cannot-link, and then maximize the information provided by each bit according to both the labeled data and the unlabeled data. Different from previous works on semi-supervised hashing, we use the square of the Euclidean distance to measure the Hamming distance, which leads to a more general Laplacian matrix based solution after the relaxation by removing the binary constraints. We also relax the orthogonality constraints to reduce the error when converting the real-value solution to the binary one. The experimental evaluations on three benchmark datasets show the superior performance of the proposed method over the state-of-the-art approaches.