Semi-supervised spectral hashing for fast similarity search

摘要

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 unsuper-vised, 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.