Recently with the explosive growth of visual content on the Internet, large-scale image search has attracted intensive attention. It has been shown that mapping high-dimensional image descriptors to compact binary codes can lead to considerable efficiency gains in both storage and performing similarity computation of images. However, most existing methods still suffer from expensive training devoted to large-scale binary code learning. To address this issue, we propose a sub-selection based matrix manipulation algorithm, which can significantly reduce the computational cost of code learning. As case studies, we apply the sub-selection algorithm to several popular quantization techniques including cases using linear and nonlinear mappings. Crucially, we can justify the resulting sub-selective quantization by proving its theoretic properties. Extensive experiments are carried out on three image benchmarks with up to one million samples, corroborating the efficacy of the sub-selective quantization method in terms of image retrieval.

中文翻译：

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用于大规模图像搜索中学习二进制代码的次选择量化

近来，随着互联网上视觉内容的爆炸性增长，大规模图像搜索引起了广泛的关注。已经表明，将高维图像描述符映射到紧凑的二进制代码可以在图像的存储和执行相似度计算方面带来可观的效率提高。但是，大多数现有方法仍然要进行专门用于大规模二进制代码学习的昂贵培训。为了解决这个问题，我们提出了一种基于子选择的矩阵处理算法，该算法可以显着降低代码学习的计算成本。作为案例研究，我们将子选择算法应用于几种流行的量化技术，包括使用线性和非线性映射的案例。至关重要的是，我们可以通过证明其理论性质来证明产生的亚选择性量化的合理性。