Binary code learning techniques have recently been actively studied for hashing based nearest neighbor search in computer vision applications due to its merit of improving hashing performance. Currently, hashing based methods can obtain good binary codes but some data may suffer from the problem of being mapped to inappropriate Hamming codes. To address this issue, this paper proposes a novel binary code learning method via iterative distance adjustment to improve traditional hashing methods, in which we utilize very short additional binary bits to correct the spatial relationship among data points and thus enhance the similarity-preserving power of binary codes. We carry out image retrieval experiments on the well-recognized benchmark datasets to validate the proposed method. The experimental results have shown that the proposed method achieves better hashing performance than the state-of-the-art binary code learning methods.
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- Binary Code Learning via Iterative Distance Adjustment
- Springer International Publishing