基于颜色特征的图像检索技术研究
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摘要
在基于内容的图像检索中,颜色特征计算简单,具有较好的鲁棒性,作为图像的一种重要视觉信息,已经得到了广泛的应用。本文主要针对图像的颜色空间、颜色的量化和颜色特征提取方法进行了研究。
首先,比较了RGB和HSV两种颜色空间模型,对颜色量化方法作了分类介绍。在此基础上重点讨论和比较了常见的颜色特征和颜色空间信息的提取方法,并对颜色特征的相似性度量方法作了介绍。
其次,针对颜色直方图缺少颜色的空间分布信息,特征维数较多,存储量大的缺点,作了如下改进:(1)为了获取颜色的空间分布信息,提出一种等面积环形分块方法,该方法不仅保持了颜色直方图的旋转不变性,而且能够突出图像中心的主体地位,符合人的视觉特性。(2)改进了采用K-means算法提取图像主颜色的方法,较好地提取了图像的主颜色直方图,既降低了特征维数,又较好地保持了颜色表示精度。(3)基于上述研究,提出了基于分块主颜色的图像检索方法:对图像进行等面积环形分块,统计各子块的主颜色直方图。该检索方法同时具有等面积环形分块和主颜色直方图的优点。实验证明,分块主颜色方法能明显地改善检索效果。(4)为了进一步提高检索效率,综合分块主颜色方法和分块颜色矩方法进行检索。实验表明,组合检索比单一的检索方法更有效。
最后,详细设计和实现基于上述方法的图像检索原型系统IRS,并基于此原型系统,对本文提出的几种图像检索方法进行了实验比较和性能分析。
As an important vision information of image, the color features are simple and have better robustness, which has been broadly used in the Content-Based Image Retrieval (CBIR).The expression of image color space, the color quantification and color feature extraction method are analyzed and discussed in this paper.
First, it compares RGB and HSV color space model, introduces the color quantification methods. On this basis, the extraction methods of the common color features and the color-spatial distribution information are analyzed and discussed emphatically.
Second, the color histogram lacks the color-spatial distribution information, has many feature dimensions and large memory capacity shortcomings. In this paper, the following improvements are made: (1) In order to obtain the color-spatial distribution information, an equal-area ring partition is proposed. The method not only keeps the color histogram rotation invariant, but also focuses on the dominant position of the image center, which is conformed to the visual characteristic. (2) The extraction method of the main color which uses the K-means algorithm is improved. The improved method extracts the main color histogram well, which reduced the number of features and maintained the color expression precision well. (3) Based on the above research, a partition main color image retrieval algorithm is proposed: partition the image using the equal-area ring method and statistic the partition's main color histogram. This retrieval algorithm has advantages of both the equal-area ring partition and main color histogram. The experiment shows that the partition main color method can improve the retrieval results. (4) To further improve the efficiency, integrated the partition main color method and partition color moment methods. The experiment shows that the combination of search retrieval method is more effective than the single.
Finally, an image retrieval prototype system (IRS) is detailed designed and implemented. Based on this prototype system, the image retrieval methods, which are proposed in this paper, are carried on and analyzed the algorithm’s performance
The theoretical analysis and experimental results show that the partition main color image retrieval method can improve the retrieval accuracy and improve the retrieval results.
首先,比较了RGB和HSV两种颜色空间模型,对颜色量化方法作了分类介绍。在此基础上重点讨论和比较了常见的颜色特征和颜色空间信息的提取方法,并对颜色特征的相似性度量方法作了介绍。
其次,针对颜色直方图缺少颜色的空间分布信息,特征维数较多,存储量大的缺点,作了如下改进:(1)为了获取颜色的空间分布信息,提出一种等面积环形分块方法,该方法不仅保持了颜色直方图的旋转不变性,而且能够突出图像中心的主体地位,符合人的视觉特性。(2)改进了采用K-means算法提取图像主颜色的方法,较好地提取了图像的主颜色直方图,既降低了特征维数,又较好地保持了颜色表示精度。(3)基于上述研究,提出了基于分块主颜色的图像检索方法:对图像进行等面积环形分块,统计各子块的主颜色直方图。该检索方法同时具有等面积环形分块和主颜色直方图的优点。实验证明,分块主颜色方法能明显地改善检索效果。(4)为了进一步提高检索效率,综合分块主颜色方法和分块颜色矩方法进行检索。实验表明,组合检索比单一的检索方法更有效。
最后,详细设计和实现基于上述方法的图像检索原型系统IRS,并基于此原型系统,对本文提出的几种图像检索方法进行了实验比较和性能分析。
As an important vision information of image, the color features are simple and have better robustness, which has been broadly used in the Content-Based Image Retrieval (CBIR).The expression of image color space, the color quantification and color feature extraction method are analyzed and discussed in this paper.
First, it compares RGB and HSV color space model, introduces the color quantification methods. On this basis, the extraction methods of the common color features and the color-spatial distribution information are analyzed and discussed emphatically.
Second, the color histogram lacks the color-spatial distribution information, has many feature dimensions and large memory capacity shortcomings. In this paper, the following improvements are made: (1) In order to obtain the color-spatial distribution information, an equal-area ring partition is proposed. The method not only keeps the color histogram rotation invariant, but also focuses on the dominant position of the image center, which is conformed to the visual characteristic. (2) The extraction method of the main color which uses the K-means algorithm is improved. The improved method extracts the main color histogram well, which reduced the number of features and maintained the color expression precision well. (3) Based on the above research, a partition main color image retrieval algorithm is proposed: partition the image using the equal-area ring method and statistic the partition's main color histogram. This retrieval algorithm has advantages of both the equal-area ring partition and main color histogram. The experiment shows that the partition main color method can improve the retrieval results. (4) To further improve the efficiency, integrated the partition main color method and partition color moment methods. The experiment shows that the combination of search retrieval method is more effective than the single.
Finally, an image retrieval prototype system (IRS) is detailed designed and implemented. Based on this prototype system, the image retrieval methods, which are proposed in this paper, are carried on and analyzed the algorithm’s performance
The theoretical analysis and experimental results show that the partition main color image retrieval method can improve the retrieval accuracy and improve the retrieval results.
引文
[1] Rui Y.Image Retrieval:Current Techniques,Promising Direction and Open Issues.Journal of Visual Communication and Image Representation,1999;10(3):39-62
[2] Aslandogan Y A,Yu C T.Techniques and Systems for Image and Video Retrieval.IEEE Trans on Knowledge and Data Engineering,1999;11(1):56-60
[3] 孔祥琴,阎放,叶丁.基于内容的图像信息检索技术的原理分析.情报科学,2002;12(12):1300-1301
[4] 孟繁杰,郭宝龙.CBIR 关键技术研究.计算机应用研究,2004;7:21~24
[5] 张宜.基于内容的图像检索技术研究综述.广西广播电视大学学报,2003;14(3):25-28
[6] Ashely J,Barger R,Flikner M,et al.Automatic and Semi-Automatic Methods for Image Annotation and Retrieval in QBIC.SPIE 2420,1995:24-35
[7] Rui Y,Huang T S,Mehrotra S.Relevance feedback:a Powerful tool in interactive content-based image retrieval.IEEE Trans. On CSVT,1998;8 (5):644-655
[8] Rui Y,Huang T S.Optimizing learning in image retrieval.IEEE Conf. On CVPR, South Carolina,USA,2000:538-547
[9] Cox I J,Miller M L,et al .The Bayesian image retrieval system,Pi Hunter: theory,implementation and Psychophysical experiments,IEEE Trans.On Image Processing,2000;9(1):20-37
[10] M Flickner, H Sawhney,et al.Query by image and video content:The QBIC system.IEEE Computer,1995;28(9):23-32
[11] R B Jeffrey et al.The Virage Image Search Engine:An Open Framework for Image Management,Proc.SPIE Storage and Retrieval for Image and Video Database,1996:57-71
[12] J R Smith ,S F Chang.VisualSEEK:A Fully Automated Content-based Query System.ACM Multimedia 96,Boston,MA,1996:417-425
[13] W Y Ma,B S Manjunath.Netra:A toolbox for navigating large image databases,Multimedia Systems,1999;7(3):184-198
[14] 庄越挺,潘云鹤.基于内容的图像检索综述.模式识别与人工智能,1999;12(2):170-177
[15] 崔屹.数字图像处理技术与应用.北京:北京电子工业出版社,1997:25-47
[16] 魏宝刚,李向阳,潘云鹤.彩色图像分割研究进展.计算机科学,1999;26(4):59-62
[17] 张恒博,欧宗瑛.一种利用多特征向量的彩色图像检索方法.计算机工程与应用,2004;2:42~43.
[18] 陈纯等.计算机图像处理技术与算法.北京:清华大学出版社,2003,125-189
[19] 田玉敏,林高全.基于颜色特征的彩色图像检索方法.西安电子科技大学学报,2002;29(1):43-46
[20] 杨晓强,基于内容的图像检索关键技术研究:[硕士学位论文],西安电子科技大学,西安:2003
[21] Pei S C, Cheng C M.Extracting Color Features and Dynamic Matching for Image Data-base Retrieval.IEEE Trans on Circuits and Systems for Video Technology,1999;9(3):501-512
[22] 耿国华,周明全.常用色彩量化算法的性能分析.小型微型计算机系统,1998;19 (9):46-49
[23] 边肇棋,张学.模式识别.北京:清华大学出版社,1999:56-57
[24] Venkateswarlu N B,Raju PSVS.Fast ISODATA Clustering Algorithm,Pattern Recognition,1992;25(3):335-342
[25] Duda R O,Hart P E.Pattern Classification and Scene Analysis.New York:John Wiley&Sons,Inc.1970:788-796
[26] 王文惠,王展,周良柱.基于内容的彩色图像颜色特征的抽取方法.计算机辅助设计与图形学学报,2001;13 (6):566-569
[27] M J Swain,D H Ballard.Color Indexing.Intl.J.of Computer Vision,1991;7(1):11~32
[28] 薛向阳,罗航哉.一种新的颜色相似度定义及其计算方法.计算机学报,1999;22(9):918-922
[29] 李贤成.基于主颜色的图像检索技术研究.交通与计算机,2006;24(4):86-88
[30] Jain A K,Vailaya A.Image retrieval using color and shape.Pattern Recognition,1996;29 (8):1233- 1244
[31] Smith J R.Integrated spatial and feature image systems:Retrieval,analysis and compression.Columbia:Columbia University,http://isney. ctr. columbia. Edu/jrsthesis,1997
[32] 王涛,胡事民,孙家广.基于颜色-空间特征的图像检索.软件学报,2002;13(10):2031-2036
[33] 章毓晋.基于内容的视觉信息检索.北京:科学出版社,2003:57-81
[34] M Stricker,M Orengo.Similarity of color images.SPIE Storage and Retrieval for Image and Video Databases III,2185:381-392
[35] G Pass,R Zabih,J Miller.Comparing images using color coherence vectors.4th ACM Conf on Multimedia.Boston:ACM,1996:65-73
[36] 黄祥林.图像检索中的关键技术.测控技术,2002;21(5):91~96
[37] A R Appas,A M Darwish,et al.Image indexing using composite regional color channels features.In:Proc.of SPIE Storage and Retrieval for Image and Video DatabaseⅦ.San Jose,CA,1999;3656:492-500
[38] L J Guibas,B Rogott,C Tomasi.Fixed-window image descriptors for image retrieval.In:Proc.of SPIE Storage and Retrieval for Image and Video Database Ⅲ.San Diego/La Jola,CA,1995;2420:352-362
[39] N Sebe,M Slew, D P Huijsmans.Multi-scale sub-image search.In:Proc.of 7th ACM Intl. Conf.(part2) on Multimedia.1999:79 -82
[40] J Li, J Z Wang,G Wiederhold.Integrated region matching for image retrieval.In:Proc.of ACM Multimedia.Los Angeles,CA,2000:147-156
[41] C Carson,M Thomas,et al.Blobworld:A system for region-based image indexing and retrieval.In:Proc.of VISUAL'99.Amsterdam,Netherlands.1999:509-516
[42] J Z Wang,G Wiederhold,et al.Wavelet-based image indexing techniques with partial sketch retrieval capability . In : Proc . of 4th ADL.Washington D.C.1997:13-24
[43] MPEG-7 Visual part of experimentation Model Version 4.0,ISO/IEC JTC1/SC29/WG11 N3068,1999
[44] J R Smith,S F Chang.Joint adaptive space and frequency graph basis selection.In:Proc.of ICIP'97.Santa Baxbaxa,CA.1997:702-705
[45] 汪慧兰,赵海峰,罗斌.基于局部颜色空间特征的图像检索.计算机技术与发展.2006;16(1):76-79
[46] 孙兴华,郭丽,郭跃飞等.基于目标区域的彩色图像检索研究.计算机研究与发展,2001;38 (9):1112-1119
[47] 韦娜,耿国华,周明全.结合颜色和空间信息的图像检索算法.计算机应用与软件,2003;20 (8):3-4
[48] 周兵,沈钧毅,彭勤科.一种新的基于对称色彩空域特征的图像匹配方法.小型微型计算机系统,2004;26(1):147-150
[49] 袁昕,朱淼良.基于主色匹配的图像检索系统.计算机辅助设计与图形学学报,2000;12(12):917-921
[50] 何清法,李国杰.综合分块主色和相关反馈技术的图像检索方法.计算机辅助设计和图形学报,2001;13(10):912-917
[51] 刘毅,张明.局部颜色特征在基于内容的图像检索中的应用.计算机应用,2004;24(7):47-49
[52] 陶丹,申铉京.基于内容的图像检索系统的关键技术.北华大学学报,2004;5(1):91-96
[53] 刘忠伟,章毓晋.基于特征的图像查询和检索系统.应用基础与工程科学学报,2000;8(1):69-76
[54] 黄元元,徐蔚鸿,杨静宇.一种基于主色调匹配的图像检索方法.计算机应用研究,2003;(1):102-104
[2] Aslandogan Y A,Yu C T.Techniques and Systems for Image and Video Retrieval.IEEE Trans on Knowledge and Data Engineering,1999;11(1):56-60
[3] 孔祥琴,阎放,叶丁.基于内容的图像信息检索技术的原理分析.情报科学,2002;12(12):1300-1301
[4] 孟繁杰,郭宝龙.CBIR 关键技术研究.计算机应用研究,2004;7:21~24
[5] 张宜.基于内容的图像检索技术研究综述.广西广播电视大学学报,2003;14(3):25-28
[6] Ashely J,Barger R,Flikner M,et al.Automatic and Semi-Automatic Methods for Image Annotation and Retrieval in QBIC.SPIE 2420,1995:24-35
[7] Rui Y,Huang T S,Mehrotra S.Relevance feedback:a Powerful tool in interactive content-based image retrieval.IEEE Trans. On CSVT,1998;8 (5):644-655
[8] Rui Y,Huang T S.Optimizing learning in image retrieval.IEEE Conf. On CVPR, South Carolina,USA,2000:538-547
[9] Cox I J,Miller M L,et al .The Bayesian image retrieval system,Pi Hunter: theory,implementation and Psychophysical experiments,IEEE Trans.On Image Processing,2000;9(1):20-37
[10] M Flickner, H Sawhney,et al.Query by image and video content:The QBIC system.IEEE Computer,1995;28(9):23-32
[11] R B Jeffrey et al.The Virage Image Search Engine:An Open Framework for Image Management,Proc.SPIE Storage and Retrieval for Image and Video Database,1996:57-71
[12] J R Smith ,S F Chang.VisualSEEK:A Fully Automated Content-based Query System.ACM Multimedia 96,Boston,MA,1996:417-425
[13] W Y Ma,B S Manjunath.Netra:A toolbox for navigating large image databases,Multimedia Systems,1999;7(3):184-198
[14] 庄越挺,潘云鹤.基于内容的图像检索综述.模式识别与人工智能,1999;12(2):170-177
[15] 崔屹.数字图像处理技术与应用.北京:北京电子工业出版社,1997:25-47
[16] 魏宝刚,李向阳,潘云鹤.彩色图像分割研究进展.计算机科学,1999;26(4):59-62
[17] 张恒博,欧宗瑛.一种利用多特征向量的彩色图像检索方法.计算机工程与应用,2004;2:42~43.
[18] 陈纯等.计算机图像处理技术与算法.北京:清华大学出版社,2003,125-189
[19] 田玉敏,林高全.基于颜色特征的彩色图像检索方法.西安电子科技大学学报,2002;29(1):43-46
[20] 杨晓强,基于内容的图像检索关键技术研究:[硕士学位论文],西安电子科技大学,西安:2003
[21] Pei S C, Cheng C M.Extracting Color Features and Dynamic Matching for Image Data-base Retrieval.IEEE Trans on Circuits and Systems for Video Technology,1999;9(3):501-512
[22] 耿国华,周明全.常用色彩量化算法的性能分析.小型微型计算机系统,1998;19 (9):46-49
[23] 边肇棋,张学.模式识别.北京:清华大学出版社,1999:56-57
[24] Venkateswarlu N B,Raju PSVS.Fast ISODATA Clustering Algorithm,Pattern Recognition,1992;25(3):335-342
[25] Duda R O,Hart P E.Pattern Classification and Scene Analysis.New York:John Wiley&Sons,Inc.1970:788-796
[26] 王文惠,王展,周良柱.基于内容的彩色图像颜色特征的抽取方法.计算机辅助设计与图形学学报,2001;13 (6):566-569
[27] M J Swain,D H Ballard.Color Indexing.Intl.J.of Computer Vision,1991;7(1):11~32
[28] 薛向阳,罗航哉.一种新的颜色相似度定义及其计算方法.计算机学报,1999;22(9):918-922
[29] 李贤成.基于主颜色的图像检索技术研究.交通与计算机,2006;24(4):86-88
[30] Jain A K,Vailaya A.Image retrieval using color and shape.Pattern Recognition,1996;29 (8):1233- 1244
[31] Smith J R.Integrated spatial and feature image systems:Retrieval,analysis and compression.Columbia:Columbia University,http://isney. ctr. columbia. Edu/jrsthesis,1997
[32] 王涛,胡事民,孙家广.基于颜色-空间特征的图像检索.软件学报,2002;13(10):2031-2036
[33] 章毓晋.基于内容的视觉信息检索.北京:科学出版社,2003:57-81
[34] M Stricker,M Orengo.Similarity of color images.SPIE Storage and Retrieval for Image and Video Databases III,2185:381-392
[35] G Pass,R Zabih,J Miller.Comparing images using color coherence vectors.4th ACM Conf on Multimedia.Boston:ACM,1996:65-73
[36] 黄祥林.图像检索中的关键技术.测控技术,2002;21(5):91~96
[37] A R Appas,A M Darwish,et al.Image indexing using composite regional color channels features.In:Proc.of SPIE Storage and Retrieval for Image and Video DatabaseⅦ.San Jose,CA,1999;3656:492-500
[38] L J Guibas,B Rogott,C Tomasi.Fixed-window image descriptors for image retrieval.In:Proc.of SPIE Storage and Retrieval for Image and Video Database Ⅲ.San Diego/La Jola,CA,1995;2420:352-362
[39] N Sebe,M Slew, D P Huijsmans.Multi-scale sub-image search.In:Proc.of 7th ACM Intl. Conf.(part2) on Multimedia.1999:79 -82
[40] J Li, J Z Wang,G Wiederhold.Integrated region matching for image retrieval.In:Proc.of ACM Multimedia.Los Angeles,CA,2000:147-156
[41] C Carson,M Thomas,et al.Blobworld:A system for region-based image indexing and retrieval.In:Proc.of VISUAL'99.Amsterdam,Netherlands.1999:509-516
[42] J Z Wang,G Wiederhold,et al.Wavelet-based image indexing techniques with partial sketch retrieval capability . In : Proc . of 4th ADL.Washington D.C.1997:13-24
[43] MPEG-7 Visual part of experimentation Model Version 4.0,ISO/IEC JTC1/SC29/WG11 N3068,1999
[44] J R Smith,S F Chang.Joint adaptive space and frequency graph basis selection.In:Proc.of ICIP'97.Santa Baxbaxa,CA.1997:702-705
[45] 汪慧兰,赵海峰,罗斌.基于局部颜色空间特征的图像检索.计算机技术与发展.2006;16(1):76-79
[46] 孙兴华,郭丽,郭跃飞等.基于目标区域的彩色图像检索研究.计算机研究与发展,2001;38 (9):1112-1119
[47] 韦娜,耿国华,周明全.结合颜色和空间信息的图像检索算法.计算机应用与软件,2003;20 (8):3-4
[48] 周兵,沈钧毅,彭勤科.一种新的基于对称色彩空域特征的图像匹配方法.小型微型计算机系统,2004;26(1):147-150
[49] 袁昕,朱淼良.基于主色匹配的图像检索系统.计算机辅助设计与图形学学报,2000;12(12):917-921
[50] 何清法,李国杰.综合分块主色和相关反馈技术的图像检索方法.计算机辅助设计和图形学报,2001;13(10):912-917
[51] 刘毅,张明.局部颜色特征在基于内容的图像检索中的应用.计算机应用,2004;24(7):47-49
[52] 陶丹,申铉京.基于内容的图像检索系统的关键技术.北华大学学报,2004;5(1):91-96
[53] 刘忠伟,章毓晋.基于特征的图像查询和检索系统.应用基础与工程科学学报,2000;8(1):69-76
[54] 黄元元,徐蔚鸿,杨静宇.一种基于主色调匹配的图像检索方法.计算机应用研究,2003;(1):102-104