LAUSR

Learning based hashing approaches have achieved considerable success in large-scale image retrieval due to the query effectiveness and efficiency. However, most studies highly rely on supervised knowledge like data labels, thus might fail in unsupervised setting. To address this issue, we propose a self-collaborative unsupervised hashing method (SCUH), which jointly learns hashing function and virtual labels in a self-collaborative manner. Different from existing unsupervised counterparts, SCUH treats sample features and the learned virtual labels as two groups of different features in a fair view. This results in two groups of transformation matrices, which have proven beneficial for capturing diversity information of different features to better advance binary codes. Moreover, we devise an alternate optimization algorithm to solve SCUH, in which each sub-problem has a closed-form solution as a byproduct. Experiments of image retrieval on three datasets show that SCUH performs favorably over eight comparative methods including unsupervised counterparts.