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基于主色选择的CBIR检索 被引量:5

CBIR RETRIEVAL BASED ON DOMINANT COLOR SELECTION
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摘要 基于内容的图像检索 (CBIR)是多媒体检索研究的前沿课题 .利用颜色特征作为索引进行图像检索是最重要的技术 .在提取图像主要颜色特征的基础上 ,进一步提取了相应的主色空间分布信息——主色矩特征 ,作为图像库的索引 .在改进加权二次型相似性度量方法的基础上 ,提出了相应的主色多特征相似性度量方法 .由于用户对图像中不同的主色具有不同的检索要求 ,提出了主色调选择的用户模型 ,用于更精确的图像检索 .实现了 WWW发布方式的 CBIR原型系统 ,实验结果表明加入主色选择使得图像检索的效果更好 . Content-based image retrieval (CBIR) is an advanced project in the recent research area of multimedia retrieval. Indexing by color features is the important technology of image retrieval. In this paper, a new kind of image feature, dominant color moment (DCM), which reflects spatial information of images, is taken into account on the basis of the image feature of dominant color (DC). A method of similarity measurement is discussed based on the improvement of quadric form distance function. A kind of interactive model between man and system, called dominant color selection, is also proposed for precise retrieval according to different requirements imposed by different dominant colors in images submitted by users. Such a system shows good effect in the CBIR experimental system implemented by means of WWW distribution.
出处 《计算机研究与发展》 EI CSCD 北大核心 2002年第9期1120-1126,共7页 Journal of Computer Research and Development
基金 国家自然科学基金 ( 6 9875 0 15 ) 浙江省自然科学基金 ( 11110 1N2 980 9)资助
关键词 主色选择 CBIR检索 主色矩 图像检索 图像数据库 计算机 content based image retrieval (CBIR), dominant color (DC), dominant color moment (DCM), relevance feedback, dominant color selection model
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  • 1Wu Xiaolin,Graphics Gems II,1991年,126页
  • 2张尧庭,多元统计分析引论,1982年,440页
  • 3Shared Mehrotra,Proceedings ofIEEE International Conference on Multimedia Computing andSystems’,1997年,632页
  • 4Smith J R,The 4th ACM International Multimedia Conference 96 Proceedings (ACM Multimedia9,1996年,87页
  • 5徐旭,朱淼良,梁倩卉.一种用于CBIR系统的主色提取及表示方法[J].计算机辅助设计与图形学学报,1999,11(5):385-388. 被引量:28

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  • 1Niblack W, Barber R, Equitz W, et al. The QBIC project:Querying images by content using color, texture, and shape[J]. San Joes, CA, 1993:173 ~ 187.
  • 2Mikolajezyk K,Schmid C. A performance evaluation of local descriptors[A]//Proeeedings of IEEE International Conference on Computer Vision and Pattern Recognition[C]. Madison, IEEE, 2003 : 1403-1410.
  • 3Lowe D. Object recognition from local scale - invariant features [A]//Proceedings oS International Conference on Computer Vision[C]. Vancouver, ICCV, 1999: 1150-1157.
  • 4Lowe D. Distinctive image features from scale - invariant key - points[J]. International Journal of Computer Vision, 2004, 60 (2):91-110.
  • 5Ke Y,Sukthankar R. PCA-SIFT: A More Distinctive Representation for Local Image Descriptors [A]// Proceedings of the IEEE Computer Society Conference[C]. Washington DC, IEEE, 2004:511-517.
  • 6Abdel-Hakim E, Farag A. CSIFT: A SIFT Descriptor with Color Invariant Characteristics[A]//Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition[C]. New York, IEEE, 2006:1978-1983.
  • 7Bosch A, Zisserman A, Munoz X. Scene classification via pLSA [A],//Proceedings of the European Conference on Computer Vision[C]. Graz, ECCV, 2006 : 517-530.
  • 8[4]W Niblack, R Barber, W Equitz, et al.. The QBIC project:Querying images by content using color, texture, and shape, San Joes, CA, 1993,173~187.
  • 9[6]孙即祥.数字图像处理.石家庄:河北教育出版社,1991.
  • 10Bach J R, FuUer C, Gupta A el al. Virage image search engine: An open framework for image management. In: SPIE 2670(23). San Jose, CA, 1996, 76-87.

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