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摘要:

图像获取和存储技术的进步使人们每天都可以获取大量的包含很多对有用的信息图像数据,但缺乏有效的工具分析这些数据。图像数据挖掘的任务就是分析、提取海量图像中隐含的有用的信息和模式,发现图像数据间的关系。图像数据挖掘并不只是数据挖掘在图像领域的简单应用,它是一门包括计算机视觉,图像处理,图像检索,数据挖掘,机器学习,数据库和人工智能等的综合学科。本文将介绍现有的图像数据挖掘的模型和技术。

Abstract:

Advances in image acquisition and storage technology have led to tremendous growth in significantly large and detailed image databases. These images reveal large and useful information to human users. But the problem is there are no useful tools to found them. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an interdisciplinary endeavor that draws upon expertise in computer vision, image processing, image retrieval, data mining, machine learning, database, and artificial intelligence. We will describe the frameworks and techniques of image mining in this paper.

参考文献

[1] M. C. Burl et al. Mining for image content. In Systemics, Cybernetics, and Informatics /Information Systems: Analysis and Synthesis, (Orlando, FL), July 1999.

[2] J. P. Eakins and M. E. Graham. Content-based image retrieval: a repot to the J ISC technology applications program. Northumbria Image Data Research Institute, 1999.

[3] Mihai Datcu et al. Bayesian Methods: Applications in information aggregation and image data mining. Intematinal Archivers of Photogrammetry and Remote Sensing, Vol. 32, Part 7-4-3 W6, Valladoloid, Spain, 4-4 June,1999.

[4] Michael C. Burl et al. Diamond Eye: A Distributed Architecture for Image Data Mining. In SPIE DMKD, Orlando, April 1999.

[5] Peter Stanchev et al. Using Image Mining for Image retrieval. LASTED conf. "Computer Science and Technology", May 19-21, 2003 Cancum, Mexico, 214-218.

[6] Peter Stanchev et al. Using Image Mining for Image retrieval. LASTED conf. "C Gibson, S et al. Intelligent mining in image databases, with applications to satellite imaging and to web search, Data Mining and Computational Intelligence", Springer-Verlag, Berlin, 2001.

[7] R. F. Cromp and W. J. Campbell. Data mining of multi-dimensional remotely sensed images. International Conference on Information and Knowledge Management (CIKM), 1993.

[8] "Computer Science and Technology", May 19-21, 2003 Cancum, Mexico, 214-218.

[9] N. Katayama and S. Satoh. The SR-tree: An index structure for high-dimensional nearest neighbor queries. In proceedings of the 1997 ACM SIGMOD Conference, pages 369-380, Tucson, Arizona, Mayl997.

[10] J. Zhang, W. Hsu and M. L. Lee. An Information-driven Framework for Image Mining, in Proceedings of 12th International Conference on Database and Expert Systems Applications (DEXA), Munich, Germany, September 2001.

[11] S. Haykin. Neural Networks: a comprehensive foundation. Prentice Hall International, Inc. 1999.

[12] K. Lin, H. V. Jagadish and C. Faloutsos. The TVtree: An index structure for high-dimensional data. The VLDB Journal, 1994,3 (4) : 517-542.

[13] T. Sellis, N. Roussopoulos and C. Faloutsos. The R + tree: A dynamic index for multi-dimensional objects. In Proc 12th VLDB, 1987.

[14] C. Ordonez and E. Omiecinski. Discovering association rules based on image content. Proceedings of the IEEE Advances in Digital Libraries Conference (ADL' 99) , 1999.

[15] A.Vailaya, A. T. Figueiredo, A. K. Jain, and H. J. Zhang. Image classification for content-based indexing. IEEE Transactions on Image Processing, Volume: 10 Issue: 1, pp117-130, Jan. 2001.

[16] D. White and R. Jain. Similarity indexing: Algorithms and performance. In Proc. SPIE Storage and Retrieval for image and Video Databases, 1996.

[17] R. Jain, R. Kasturi and B. G. Schunck. Machine Ver- sion. MIT Press and McGraw-Hill Press, 1995.

[18] J. Z. Wang, J. Li et al. System for Screening Objectionable Images Using Daubechies" Wavelets and Color Histograms. Interactive Distributed Multimedia Systems and Telecommunication Services, Proceedings of the Fourth European Workshop (IDMS'97) , 1997.

[19] E. Chang, C. Li and J Wang. Searching Near-Replicas of Image via Clustering. SPIE Multimedia Storage and Archiving Systems VI, Boston, MA, USA, 1999.

[20] J. T. Robinson. The K-D-B tree: A search structure for large multidimensional dynamic indexes. In Proceeding of the 1981 ACM SIGMOD Conference, pages 10-18, June 1981.

[21] W. Y. Ma and B. S. Manjunath. A texture thesaurus for browsing large aerial photographs, Journal of the American Society for Information Science 49(7) :633-648,1998.

[22] K. L. Tan, B.C. Ooi and L. F. Thiang. Retrieving similar shapes effectively and efficiently, Multimedia Tools and Applications, Kluwer Academic Publishers, The Netherlands, 2001. [32] K. L Tan, B. C. Ooi and C. Y. Yee. An Evaluation of Color-Spatial Retrieval Techniques for Large Image Databases, Multimedia Tools and Applications, Vol. 14, No. 1, pp. 55-78, Kluwer Academic Publishers, The Netherlands, 2001.

基本信息:

DOI:10.13682/j.issn.2095-6533.2004.03.020

中图分类号:TP391.41

引用信息:

[1]薄华,马缚龙,焦李成.图像数据挖掘的模型和技术[J].西安邮电学院学报,2004(03):81-85.DOI:10.13682/j.issn.2095-6533.2004.03.020.

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