Abstract
Hashing methods have been widely used in large-scale image retrieval. However, the constraints on the hash codes of similar images learned by the previous hashing methods are too strong, which may lead to overfitting and difficult convergence. Besides, the binary codes output by the previous hashing methods are not optimally compatible with the multi-index approach, which is the most effective method for Hamming distance query acceleration. In this paper, we propose a novel Robust and Index-Compatible Deep Hashing (RICH) method to learn compact similarity-preserving binary codes, which focuses on improving the retrieval accuracy and time efficiency simultaneously. With the learned binary codes, we can achieve better results compared with the state-of-the-arts in retrieval accuracy. Meanwhile, remarkable promotions of the retrieval time efficiency have been made in the Hamming distance query process.
Published Version
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