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首页> 外文期刊>Journal of visual communication & image representation >Fast indexing method for image retrieval using k nearest neighbors searches by principal axis analysis
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Fast indexing method for image retrieval using k nearest neighbors searches by principal axis analysis

机译：利用主轴分析的k个最近邻搜索进行图像检索的快速索引方法

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摘要

This paper presents a fast indexing scheme for content-based image retrieval based on the principal axis analysis. Image databases often represent the image objects as high-dimensional feature vectors and access them via the feature vectors and similarity measure. A similarity measure similar to the quadratic histogram distance measure is defined for this indexing method. The computational complexity of similarity measure in high-dimensional image database is very huge and hence the applications of image retrieval are restricted to certain areas. In this work, feature vectors in a given image are ordered by the principal axis analysis to speed up the similarity search in a high-dimensional image database using k nearest neighbor searches. To demonstrate the effectiveness of the proposed algorithm, we conducted extensive experiments and compared the performance with the IBM's query by image content (QBIC) method, Jain and Vailay's method, and the LPC-file method. The experimental results demonstrate that the proposed method outperforms the compared methods in retrieval accuracy and execution speed. The execution speed of the proposed method is much faster than that of QBIC method and it can achieve good results in terms of retrieval accuracy compared with Jain's method and QBIC method.
机译：本文提出了一种基于主轴分析的基于内容的图像检索快速索引方案。图像数据库通常将图像对象表示为高维特征向量，并通过特征向量和相似性度量对其进行访问。为此索引方法定义了类似于二次直方图距离度量的相似性度量。高维图像数据库中相似度度量的计算复杂度非常高，因此图像检索的应用仅限于某些领域。在这项工作中，通过主轴分析对给定图像中的特征向量进行排序，以加快使用k个最近邻居搜索在高维图像数据库中的相似性搜索。为了证明该算法的有效性，我们进行了广泛的实验，并将其性能与IBM的图像内容查询（QBIC）方法，Jain和Vailay方法以及LPC文件方法进行了比较。实验结果表明，该方法在检索精度和执行速度上均优于同类方法。所提方法的执行速度比QBIC方法快得多，与Jain方法和QBIC方法相比，在检索精度方面可以取得良好的效果。

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