Dual local consistency hashing with discriminative projections selection

Semantic hashing is a promising way to accelerate similarity search, which designs compact binary codes for a large number of images so that semantically similar images are mapped to close codes. Retrieving similar neighbors is then simply accomplished by retrieving images that have codes within a small Hamming distance of the code of the query. However, most of the existing hashing approaches, such as spectral hashing (SH), learn the binary codes by preserving the global similarity, which do not have full discriminative power. In this paper, we propose a dual local consistency hashing method which not only makes the similar images have the same codes but also dissimilar images with different codes. Moreover, we propose a PCA projection selecting scheme that choose the most discriminative projection for each bit of the codes. Therefore, the binary codes learned by our approach are more powerful and discriminative for similarity search. Extensive experiments are conducted on publicly available datasets and the comparison results demonstrate that our approach can outperform the state-of-art methods.

► We propose a novel hashing approach based on dual local consistency.
► We propose a discriminative projection selecting scheme to learn hash functions.
► Our method outperforms the state-of-the-art methods for fast image search.