Semi-supervised constraints preserving hashing

With the ever-increasing amount of multimedia data on the web, hashing-based approximate nearest neighbor search methods have attracted significant attention due to its remarkable efficiency gains and storage reductions. Traditional unsupervised hashing methods are designed for preserving distance metric similarity which may lead to semantic gap among the high-level semantic similarities. Recently, attentions have been paid to semi-supervised hashing methods which can preserve data׳s a few available semantic similarities (usually given in terms of labels, pairwise constraints, tags, etc.). However, these methods often preserve semantic similarities for low-dimensional embeddings. When converting low-dimensional embeddings into binary codes, the quantization error will be accumulated thus resulting in performance deterioration. To this end, we propose a novel semi-supervised hashing method which preserves pairwise constraints for both low-dimensional embeddings and binary codes. It first represents data points by cluster centers to preserve data neighborhood structure and reduce the dimensionality. Then the constraint information is fully utilized to embed the derived data representations into a discriminative low-dimensional space by maximizing discriminative Hamming distance and data variance. After that, optimal binary codes are obtained by further preserving the semantic similarities in the process of quantizing the low-dimensional embeddings. By utilizing constraint information in the quantization process, the proposed method can fully preserve pairwise semantic similarities for binary codes thus leading to better retrieval performance. Thorough experiments on standard databases show the superior performance of the proposed method.