A Lower Bound of Hash Codes' Performance

• URL: http://arxiv.org/abs/2210.05899v1
• Date: Wed, 12 Oct 2022 03:30:56 GMT
• Title: A Lower Bound of Hash Codes' Performance
• Authors: Xiaosu Zhu, Jingkuan Song, Yu Lei, Lianli Gao and Heng Tao Shen
• Abstract summary: In this paper, we prove that inter-class distinctiveness and intra-class compactness among hash codes determine the lower bound of hash codes' performance. We then propose a surrogate model to fully exploit the above objective by estimating the posterior of hash codes and controlling it, which results in a low-bias optimization. By testing on a series of hash-models, we obtain performance improvements among all of them, with an up to $26.5%$ increase in mean Average Precision and an up to $20.5%$ increase in accuracy.
• Score: 122.88252443695492
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As a crucial approach for compact representation learning, hashing has achieved great success in effectiveness and efficiency. Numerous heuristic Hamming space metric learning objectives are designed to obtain high-quality hash codes. Nevertheless, a theoretical analysis of criteria for learning good hash codes remains largely unexploited. In this paper, we prove that inter-class distinctiveness and intra-class compactness among hash codes determine the lower bound of hash codes' performance. Promoting these two characteristics could lift the bound and improve hash learning. We then propose a surrogate model to fully exploit the above objective by estimating the posterior of hash codes and controlling it, which results in a low-bias optimization. Extensive experiments reveal the effectiveness of the proposed method. By testing on a series of hash-models, we obtain performance improvements among all of them, with an up to $26.5\%$ increase in mean Average Precision and an up to $20.5\%$ increase in accuracy. Our code is publicly available at \url{https://github.com/VL-Group/LBHash}.

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