一种用于视觉搜索应用的改进人工神经网络搜索算法

Fuqiang Ma, Jing Chen, Yanfeng Tong, Lei Sun
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引用次数: 0

摘要

近似最近邻搜索是保证视觉搜索系统精度和速度的一种重要算法。本文在积量化框架下对搜索算法进行了改进。积量化可以通过积量化器生成一个指数级大的码本,然后通过非对称距离计算或对称距离计算实现快速搜索,但在计算近似距离时,在某些情况下仍会产生较大的失真。因此,我们设计了分层残差积量化方法,同时对输入和残差空间进行量化,同时对非对称距离计算进行了扩展,使这种量化方法仍然能很好地估计出近似距离。我们已经在几个数据集上测试了我们的方法,实验表明,我们的方法与最先进的方法相比,始终提高了准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An improved ANN search algorithm for visual search applications
Approximate nearest neighbor search is a kind of significant algorithm to ensure the accuracy and speed for visual search system. In this paper, we ameliorate the search algorithm following the framework of product quantization. Product quantization can generate an exponentially large codebook by a product quantizer and then achieve rapid search with the asymmetric distance computation or symmetric distance computation, while it will still produce a larger distortion in some cases when calculating the approximate distance. Therefore, we design the hierarchical residual product quantization which simultaneously quantifies the input and residual space and meanwhile we extend the asymmetric distance computation to handle this quantization method which is still very efficient to estimate the approximate distance. We have tested our method on several datasets, and the experiment shows that our method consistently improves the accuracy against the-state-of-the-art methods.
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