Dimensional Reduction Based on Independent Component Analysis for Content Based Image Retrieval

Zhi Li, Shuixiu Wu, Xiaoqing Wang, Hao Ye, Ming-Wen Wang, Jihua Ye
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引用次数: 5

Abstract

We propose a novel way to apply Independent Component Analysis (ICA) [1] on eight kinds of visual descriptors (features), and combine the eight features of the same database to extract independent component (IC) feature of each feature. A comparative study on the retrieval performance has been done between the original features and IC features in four image databases. Experiment results show that the IC features are of much less in dimension than the original features, and achieve satisfying retrieval results, sometimes even better than the original results. In this way, hardware storage can be saved in the retrieval preprocessing step.
基于独立分量分析的图像降维方法
我们提出了一种新颖的方法,将独立成分分析(ICA)[1]应用于八种视觉描述符(特征),并将同一数据库的八种特征结合起来,提取每个特征的独立成分(IC)特征。对四种图像数据库中原始特征和集成特征的检索性能进行了比较研究。实验结果表明,集成电路特征的维数比原始特征少得多,并且取得了令人满意的检索结果,有时甚至优于原始结果。这样可以在检索预处理步骤中节省硬件存储。
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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