基于HSV和纹理特征的相容near度量方法

计算机科学 ›› 2015, Vol. 42 ›› Issue (Z11): 109-112.

基于HSV和纹理特征的相容near度量方法

刘文英,王拥军,杨义川

北京航空航天大学数学与系统科学学院北京100191,北京航空航天大学数学与系统科学学院北京100191,北京航空航天大学数学与系统科学学院北京100191

出版日期:2018-11-14 发布日期:2018-11-14
基金资助:
本文受国家自然科学基金(11271040)资助

Tolerance Nearness Measure Based on HSV and Texture Feature

LIU Wen-ying, WANG Yong-jun and YANG Yi-chuan

Online:2018-11-14 Published:2018-11-14

摘要/Abstract

摘要： 基于内容的图像检索是图像处理研究的重点,而相似性度量是其核心问题。基于near集的tNM(Tolerance Nearness Measure)方法在仅提取图像的灰度值特征时比IRM(Integrated Region Matching)检索结果更好。基于tNM与人类视觉近似的特点,将灰度值替换为面向用户视觉的HSV(Hue,Saturation,Value)颜色空间,分别提取图像的灰度值(Grey)+纹理(Texture)、HSV+纹理两组特征。使用IRM和tNM算法对10类图像进行检索,对其检索结果进行比较分析,结果表明使用tNM算法提取的图像的HSV+纹理特征与人类视觉更加近似,效果更佳。

关键词: 相似性度量,相容near度量,灰度值+纹理,HSV+纹理

Abstract: Content-based image retrieval is a very important issue in image processing.Similarity measure is a core problem in content-based image retrieval.Tolerance nearness measure method based on near set is better than IRM(integra-ted region matching) when it just extracts the Grey feature.Considering that tNM is close to human visual,we replaced Grey feature with HSV color space. We extracted Grey+texture feature and HSV+texture feature,respectively.Then the retrieval results was obtained by IRM and tNM from 10 categories images.Through analyzing and comparing those results,we drew a conclusion that HSV+texture feature has higher performance compared to Grey+texture feature.

刘文英,王拥军,杨义川. 基于HSV和纹理特征的相容near度量方法[J]. 计算机科学, 2015, 42(Z11): 109-112. https://doi.org/

LIU Wen-ying, WANG Yong-jun and YANG Yi-chuan. Tolerance Nearness Measure Based on HSV and Texture Feature[J]. Computer Science, 2015, 42(Z11): 109-112. https://doi.org/

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