Abstract
Deep hashing methods have achieved promising results for large-scale image retrieval recently. To accelerate the subsequent Hamming ranking process, the multi-index approach has been proposed to reduce the computations for the Hamming distance. However, the binary codes output by the previous deep hashing methods may not be optimally compatible with the multi-index approach. In this paper, we present a novel Deep Index-Compatible Hashing (DICH) method for fast image retrieval, which can learn similarity-preserving binary codes that are more compatible with the multi-index approach. With the learned binary codes, both the size of the intermediate result set produced by the multi-index approach and the number of the candidate images can be reduced, which can accelerate the Hamming ranking process. By taking advantage of the unique feature of DICH, we further propose a block-based ranking strategy to quickly rank the candidate images without calculating the Hamming distance. Extensive evaluations demonstrate that the proposed method can significantly reduce the retrieval time with almost no loss of retrieval accuracy.
Published Version
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