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DCD用于基于内容的自然图像检索

Content-based natural image retrieval using DCD
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摘要 基于内容的图像检索是多媒体应用研究领域的一项关键技术。本文提出了一种应用MPEG-7的DCD描述进行基于内容的图像检索方法,该方法采用图像的主体颜色来代表整幅图像的颜色信息,在检索时效率更高,相对于以前的基于颜色的图像检索方法,具体实现更简便,并且能保证高质量的检索结果。 Content-based image retrieval is a key technique in multimedia. In this paper, a content-based image retrieval method using MPEG-7 Dominant Color Descriptor （DCD） is proposed. In the method, the dominant colors in images present the entire color information. Compared with the realization of former retrieval methods based on color, the method is more convenient. Experimental results have shown that the method proposed is very effective, compact and promising.
作者 屠菁 张佑生 顾红飞
机构地区 合肥工业大学计算机与信息学院图形与图像研究室
出处 《农业网络信息》 2005年第11期24-26,共3页 Agriculture Network Information
关键词 MPEG-7 主体颜色描述子 基于内容的图像检索 MPEG-7 Dominant color descriptor Content-based image retrieval
分类号 TP37 [自动化与计算机技术—计算机系统结构]
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参考文献7

  • 1B.S. Manjunath, J.-R. Ohm, V.V. Vasudevan, A.Yamada. Color and Texture Descriptors, IEEE Transaction on Circuits and Systems for Video Technology, 2001,11 (6):703～715.
  • 2Plataniotis k N, Venetsanopoulos A N.Color Image Pro-cessing and Application. Springer,2000.
  • 3付岩,王耀威,王伟强,高文.SVM用于基于内容的自然图像分类和检索[J].计算机学报,2003,26(10):1261-1265. 被引量：54
  • 4Lai-Man Po and Ka-Man Wong. A New Palette Histogram Similarity Measure For MPEG-7 Dominant Color Descriptor [EB/OL].http://mirror.yi.org/～carlos/, 2004,(12).
  • 5Y.Rui, T.S. Huang & S. Mehrotra. Content-Based Image Retrieval with Relevance Feedback in MARS.Proceedings of IEEE International Conference on Image Processing. Santa Barbara. California, 2003,(10):815～818.
  • 6ISO/IEC JTC1/SC29/WG11. 2002c. MPEG-7 overview, V.8,Doc.N4980.
  • 7ISO/IEC JTC1/SC29/WG11. 2001. Overview of the MPEG-7standard,V.6, Doc. N4509.

二级参考文献15

  • 1Burkhardt H, Siggelkow S. Invariant features for discriminating between equivalence classes. In:Nonlinear Model-based Image Video Processing and Analysis. NY: John Wiley and Sons,2000.
  • 2Scholkopf B, Smola A J. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond.Cambridge, Mass: MIT Press, 2002.
  • 3Vapnik V N. The Nature of Statistical Learning Theory. NewYork: Springer-Verlag, 2000.
  • 4Scholkopf B, Burges C J C, Smola A J. Advances in Kernel Methods—Support Vector Learning. Cambridge, MA: MIT Press, 1999.
  • 5Smeulders A, Worring M et al. Content-based image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(12) : 1349～ 1380.
  • 6Flickner M et al. Query by image and video content: The QBIC system. IEEE Computer, 1995,28(9) : 23 ～32.
  • 7Bach J R, Fuller C, Gupta Aet al. Virage image search engine: an open framework for image management. SPIE Storage and Retrieval of Image and Video DataBases, 1996,4:76 ～87.
  • 8Smith J, Chang S F. VisualSEEK: A fully automated contentbased image query system. In: Proceedings of the 4th ACM Multimedia Conference,Boston MA, USA, 1996.87～98.
  • 9Vailaya A, Figueiredo M, Jain A, Zhang H-J. A Bayesian framework for semantic classification of outdoor vacation images. In: Proceedings of SPIE:Storage and Retrieval for Image and Video Databases VII, San Jose, CA, USA, 1999,3656:415～426.
  • 10Lipson P, Grimson E, Sinha P. Configuration based scene classification and image indexing. In: Proceedings of the 16th IEEE Conference on Computer Vision and Pattern Recognition,Puerto Rico, 1997. 1007～1013.

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农业网络信息
2005年 第11期

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