一种改进的图像检索算法

Guangwen Zhang, Lei Yang, Jun Zhai, Hui Li, Yueping Lian
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引用次数: 1

摘要

本文介绍了基于颜色特征的图像检索的全过程。本文首先对HSV颜色模型进行了简要介绍,然后对HSV颜色模型的选择进行了验证,以其他环节来选择方法。其次,对色彩空间进行量化,量化标准分为等距量化和非等距量化两种。其次是选择合适的特征提取方法。一般的颜色直方图在没有颜色空间的情况下,只能表达图像的全局统计信息,因此本文采用局部累积直方图的方法,并在相似性测度的选择上,比较了欧几里得距离和加权距离,从而得出更有效的检索结果。最后,通过对比,选择了最优的图像检索算法:HSV颜色模型-局部累积直方图-欧几里得距离-等距量化。
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 Image Retrieval Algorithm
This paper introduces the whole process of retrieval based on color characteristics.First of all,this article presents the HSV color model in brief,then HSV color model is selected to verify other links to the choice of methods.Next,the color space is quantized and the quantization standard can be divided into two kinds:equidistant quantification and non-equidistant quantification.Then it is choosing proper feature extraction method.General color histogram can only express the global statistical information of the image without the color space,so this article adopts the method of local cumulative histogram,and in the choice of similarity measure,this paper compares Euclidean distance and the weighted distance,and then concludes the more effective retrieval results.Finally,through the contrast,this paper chooses the optimal image retrieval algorithm:HSV color model-Local accumulate histogram-Euclidean distance-Equidistant quantification.
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