基于t -混合模型和ICA的大规模图数据库索引

Fourth International Conference on Image and Graphics (ICIG 2007) Pub Date : 2007-08-22 DOI:10.1109/ICIG.2007.124

B. Luo, A. Zheng, Jin Tang, Haifeng Zhao

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引用次数: 72

摘要

本文提出了一种基于t-混合模型和ICA的索引方案，该方案在非典型点(或离群点)存在或数据集有重尾时比高斯混合建模具有更强的鲁棒性。该索引方案结合了优化矢量量化和基于概率近似的索引方案。在大规模图数据库上的实验结果表明，该算法的效率得到了显著提高，且精度较好。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Large-Scale Graph Database Indexing Based on T-mixture Model and ICA

This paper proposes an indexing scheme based on t- mixture model and ICA, which is more robust than Gaussian mixture modeling when atypical points (or outliers) exist or the set of data has heavy tail. This indexing scheme combines optimized vector quantizer and probabilistic approximate-based indexing scheme. Experimental results on large-scale graph database show a notable efficiency improvement with optimistic precision.

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Fourth International Conference on Image and Graphics (ICIG 2007)

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