[1]曹建芳,等.一种改进的HSV颜色空间量化方法及其应用[J].南京师范大学学报(工程技术版),2014,14(02):068.
　Cao Jianfang,Chen Junjie,et al.An Improved Method on Color Space Quantization and Application[J].Journal of Nanjing Normal University(Engineering and Technology),2014,14(02):068.
点击复制

一种改进的HSV颜色空间量化方法及其应用
分享到：

南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
14卷
期数:
2014年02期
页码:
068
栏目:
出版日期:
2014-06-30

文章信息/Info

Title:
An Improved Method on Color Space Quantization and Application
作者:
曹建芳1; 2; 陈俊杰2; 赵青杉1
(1.忻州师范学院计算机科学与技术系,山西 忻州 034000) (2.太原理工大学计算机科学与技术学院,山西 太原 030024)
Author(s):
Cao Jianfang1; 2; Chen Junjie2; Zhao Qingshan1
(1.Department of Computer Science and Technology,Xinzhou Teachers University,Xinzhou 034000,China) (2.College of Computer Science and Technology,Taiyuan University of Technology,Taiyuan 030024,China)
关键词:
图像检索; 颜色特征; HSV颜色空间; 量化处理
Keywords:
image retrieval; color feature; HueSaturationValue color space; quantification
分类号:
TP391
文献标志码:
A
摘要:
随着图像数据的海量增长,图像检索效率逐渐成为研究热点.为了提高图像检索的准确率,提出了一种改进的HSV颜色空间量化方法,细化色调H的分类,使量化结果更接近人类感知,并在此基础上采用分块策略进行仿真实验.实验结果表明,提出的方法能更好地描述图像的颜色特征,效果令人满意,具有一定的实用性.
Abstract:
With the massive growth of images,the efficiency of image retrieval gradually becomes a research hotspot.In order to improve accuracy of the image retrieval,HueSaturationValue(HSV)color space is improved,which details the classification of H hue and makes the quantitative results more close to human perception.On this basis,block color histogram is extracted as a retrieval feature.Experiments show that the proposed method can better describe color feature of images,the results are satisfactory and have some practical value.

参考文献/References:

[1]Liu Ying,Chen Xin,Zhang Chengcui,et al.Semantic clustering for regionbased image retrieval[J].J Vis Commun Image R,2008,20:157-166.
[2]Cho S B,Lee J Y.A humanoriented image retrieval system using interactive genetic algorithm[J].IEEE Trans on Systems,Man and Cybernetics,2002,32(3):452-458.
[3]李巧玲.基于内容的图像检索技术研究[D].西安:西安科技大学计算机科学与技术学院,2011.
Li Qiaoling.Research on contentbased image retrieval technology[D].Xi’an:College of Computer Science and Technology,Xi’an University of Science and Technology,2011.(in Chinese)
[4]张磊.基于机器学习的图像检索若干问题研究[D].青岛:山东大学信息科学与工程学院,2011.
Zhang Lei.Research on image retrieval with machine learning techniques[D].Qingdao:College of Information Science and Engineering,Shandong University,2011.(in Chinese)
[5]崔屹.数字图像处理技术与应用[M].北京:电子工业出版社,1997.
Cui Yi.Digital Image Processing Technology and Application[M].Beijing:Electronics Industry Publishing House,1997.(in Chinese)
[6]郭士会,杨明,王晓芳,等.基于FSRM的相关反馈图像检索算法[J].计算机科学,2012,39(s1):540-542.
Guo Shihui,Yang Ming,Wang Xiaofang,et al.Relevance feedback algorithm based on FSRM in image retrieval[J].Computer Science,2012,39(s1):540-542.(in Chinese)

相似文献/References:

[1]郑爱彬,张明.基于相关聚合直方图的CBIR[J].南京师范大学学报(工程技术版),2005,05(04):057.
　ZHENG Aibin,ZHANG Ming.The Research of the Correlation and Polymerization Histogram in CBIR[J].Journal of Nanjing Normal University(Engineering and Technology),2005,05(02):057.
[2]卞 乐,李天峰,韦 怡,等.HLBP与颜色特征自适应融合的粒子滤波目标跟踪改进算法[J].南京师范大学学报(工程技术版),2018,18(01):056.[doi:10.3969/j.issn.1672-1292.2018.01.008]
　Bian Le,Li Tianfeng,Wei Yi,et al.Improved Particle Filtering Target Tracking Algorithm forHLBP and Color Feature Adaptive Fusion[J].Journal of Nanjing Normal University(Engineering and Technology),2018,18(02):056.[doi:10.3969/j.issn.1672-1292.2018.01.008]
[3]欧丰林,吴慧君,杨文元.视频目标跟踪的颜色特征学习率优化分析[J].南京师范大学学报(工程技术版),2019,19(03):059.[doi:10.3969/j.issn.1672-1292.2019.03.009]
　Ou Fenglin,Wu Huijun,Yang Wenyuan.Optimization Analysis of Target Tracking Learning Rate via Color Feature[J].Journal of Nanjing Normal University(Engineering and Technology),2019,19(02):059.[doi:10.3969/j.issn.1672-1292.2019.03.009]

备注/Memo

备注/Memo:
收稿日期:2014-02-10.
基金项目:国家自然科学基金(61202163)、山西省自然科学基金(2013011017-2)、山西省高校科技创新项目(2013150)、忻州师范学院重点学科专项课题(XK201308).
通讯联系人:曹建芳,博士研究生,副教授,研究方向:情感计算、数字图像理解等.E-mail:kcxdj122@126.com
更新日期/Last Update: 2014-06-30