Unsupervised discriminative hashing

Hashing is one of the popular solutions for approximate nearest neighbor search because of its low storage cost and fast retrieval speed, and many machine learning algorithms are adapted to learn effective hash function. As hash codes of the same cluster are similar to each other while the hash codes in different clusters are dissimilar, we propose an unsupervised discriminative hashing learning method (UDH) to improve discrimination among hash codes in different clusters. UDH shares a similar objective function with spectral hashing algorithm, and uses a modified graph Laplacian matrix to exploit local discriminant information. In addition, UDH is designed to enable efficient out-of-sample extension. Experiments on real world image datasets demonstrate the effectiveness of our novel approach for image retrieval.