An Information-Driven Framework for Image Mining

Image mining systems that can automatically extract semantically meaningful information (knowledge) from image data are increasingly in demand. The fundamental challenge in image mining is to determine how lowlevel, pixel representation contained in a raw image or image sequence can be processed to identify high-level spatial objects and relationships. To meet this challenge, we propose an efficient information-driven framework for image mining. We distinguish four levels of information: the Pixel Level, the Object Level, the Semantic Concept Level, and the Pattern and Knowledge Level. High-dimensional indexing schemes and retrieval techniques are also included in the framework to support the flow of information among the levels. We believe this framework represents the first step towards capturing the different levels of information present in image data and addressing the issues and challenges of discovering useful patterns/knowledge from each level.

[1]  Kee Tung. Wong,et al.  Texture features for image classification and retrieval. , 2002 .

[2]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[3]  Scott A. Starks,et al.  Intelligent Mining in Image Databases , With Applications to Satellite Imagingand to Web , 2000 .

[4]  Hideyuki Tamura,et al.  Textural Features Corresponding to Visual Perception , 1978, IEEE Transactions on Systems, Man, and Cybernetics.

[5]  J. T. Robinson,et al.  The K-D-B-tree: a search structure for large multidimensional dynamic indexes , 1981, SIGMOD '81.

[6]  Wynne Hsu,et al.  Image mining in IRIS: integrated retinal information system , 2000, SIGMOD 2000.

[7]  Nick Roussopoulos,et al.  Faloutsos: "the r+- tree: a dynamic index for multidimensional objects , 1987 .

[8]  Hans-Peter Kriegel,et al.  The X-tree : An Index Structure for High-Dimensional Data , 2001, VLDB.

[9]  B. S. Manjunath,et al.  Texture Features for Browsing and Retrieval of Image Data , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Carlos Ordonez,et al.  Image Mining: A New Approach for Data Mining , 1998 .

[11]  James Ze Wang,et al.  Classifying Objectionable Websites Based on Image Content , 1998, IDMS.

[12]  Donald Ervin Knuth,et al.  The Art of Computer Programming , 1968 .

[13]  Mihai Datcu,et al.  Image information mining: exploration of image content in large archives , 2000, 2000 IEEE Aerospace Conference. Proceedings (Cat. No.00TH8484).

[14]  Beng Chin Ooi,et al.  Indexing the edges—a simple and yet efficient approach to high-dimensional indexing , 2000, PODS.

[15]  Shin'ichi Satoh,et al.  The SR-tree: an index structure for high-dimensional nearest neighbor queries , 1997, SIGMOD '97.

[16]  Abraham Kandel,et al.  Data Mining and Computational Intelligence , 2001 .

[17]  Robert F. Cromp,et al.  Data Mining of Multi-dimensional Remotely Sensed Images. , 1993, CIKM 1993.

[18]  Markus A. Stricker,et al.  Color indexing with weak spatial constraints , 1996, Electronic Imaging.

[19]  B. S. Manjunath,et al.  A Texture Thesaurus for Browsing Large Aerial Photographs , 1998, J. Am. Soc. Inf. Sci..

[20]  Donald E. Knuth,et al.  The Art of Computer Programming: Volume 3: Sorting and Searching , 1998 .

[21]  Ramesh Jain,et al.  Storage and Retrieval for Image and Video Databases III , 1995 .

[22]  Jiawei Han,et al.  Mining MultiMedia Data , 1999 .

[23]  Elisa Bertino,et al.  Indexing Techniques for Advanced Database Systems , 1997, The Springer International Series on Advances in Database Systems.

[24]  Romain Murenzi,et al.  Fast texture database retrieval using extended fractal features , 1997, Electronic Imaging.

[25]  Markus A. Stricker,et al.  Similarity of color images , 1995, Electronic Imaging.

[26]  Robert F. Cromp,et al.  Data mining of multidimensional remotely sensed images , 1993, CIKM '93.

[27]  Mong-Li Lee,et al.  Image mining in IRIS: integrated retinal information system , 2000, SIGMOD '00.

[28]  Christos Davatzikos,et al.  Mining lesion-deficit associations in a brain image database , 1999, KDD '99.

[29]  Oscar Firschein,et al.  System for Classifying Objectionable Websites , 1998 .

[30]  Antonin Guttman,et al.  R-trees: a dynamic index structure for spatial searching , 1984, SIGMOD '84.

[31]  Michael McGill,et al.  Introduction to Modern Information Retrieval , 1983 .

[32]  Hans-Peter Kriegel,et al.  The R*-tree: an efficient and robust access method for points and rectangles , 1990, SIGMOD '90.

[33]  Vladik Kreinovich,et al.  Intelligent mining in image databases, with applications to satellite imaging and to web search , 2001 .

[34]  Donald E. Knuth,et al.  Sorting and Searching , 1973 .

[35]  Christos Faloutsos,et al.  The R+-Tree: A Dynamic Index for Multi-Dimensional Objects , 1987, VLDB.