Database retrieval for similar images using ICA and PCA bases
Database retrieval for similar images using ICA and PCA bases
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DOI：
10.1016/j.engappai.2005.01.002
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发表时间：
2005-09
期刊：
影响因子：
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通讯作者：
N. Katsumata;Y. Matsuyama
中科院分区：
文献类型：
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作者：
N. Katsumata;Y. Matsuyama
相似图像检索系统是新近提出和研究的。该系统使用ICA库（独立成分分析库）或PCA库（主成分分析库）。这些基可以包含源图像的信息，但由于PCA和ICA问题表述本身的原因，基上的顺序和幅度存在不确定性。但是，本文利用相似基的加权内积成功地避免了这一困难。根据{相似性度量（ICA， PCA，颜色直方图），颜色空间（RGB， YIQ， HSV），过滤（with, without）}的组合对18个系统进行了一组意见测试。颜色直方图法是一种传统的方法。意见检验表明，{ICA， HSV，不加滤波}的方法是最好的。亚军是{ICA， HSV或RGB或YIQ，过滤}。人们认为传统的方法差得多。结果表明，本文的方法对大型数据库中相似图像的检索是非常有效的。
Similar-image retrieval systems are newly presented and examined. The systems use ICA bases (independent component analysis bases) or PCA bases (principal component analysis bases). These bases can contain source image's information, however, the indeterminacy of ordering and amplitude on the bases exists due to the PCA and ICA problem formulation per se. But, this paper successfully avoids this difficulty by using weighted inner products of similar bases. A set of opinion test is carried out on 18 systems according to the combination of {similarity measures (ICA, PCA, color histogram), color spaces (RGB, YIQ, HSV), filtering (with, without)}. The color histogram method is a traditional method. The opinion test shows that the presented method of {ICA, HSV, without filtering} is the best. Runners-up are {ICA, HSV or RGB or YIQ, with filtering}. The traditional method is judged to be much inferior. Thus, this paper's method is found quite effective to the similar-image retrieval from large databases.