Abstract
Large visual database systems require effective and efficient ways of indexing and accessing visual data on the basis of content. In this process, significant features must first be extracted from image data in their pixel format. These features must then be classified and indexed to assist efficient access to image content. With the large volume of visual data stored in a visual database, image classification is a critical step to achieve efficient indexing and retrieval. In this paper, we investigate an effective approach to the clustering of image data based on the technique of fractal image coding, a method first introduced in conjunction with fractal image compression technique. A joint fractal coding technique, applicable to pairs of images, is used to determine the degree of their similarity. Images in a visual database can be categorized in clusters on the basis of their similarity to a set of iconic images. Classification metrics are proposed for the measurement of the extent of similarity among images. By experimenting on a large set of texture and natural images, we demonstrate the applicability of these metrics and the proposed clustering technique to various visual database applications.
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M.Arya, W.Cody, C.Faloutsos, J.Richardson, and A.Toga, “QBISM: A prototype 3-D medical image database system,” IEEE Data Engineering Bulletin, Vol. 16, No. 1, pp. 38–42, 1993.
J.R.Bach, S.Paul, and R.Jain, “A visual information management system for the interactive retrieval of faces,” IEEE Transactions on Knowledge and Data Engineering, Vol. 5, No. 4, pp. 619–628, 1993.
M.F. Barnsley and A.D. Sloan, “A better way to compress images,” BYTE, pp. 215–223, 1988.
N. Beckmann, H.P. Kriegel, R. Schneider, and B. Seeger, “The R*-tree: An efficient and robust access method for points and rectangles,” in Proceedings of ACM-SIGMOD International Conference on Management of Data, Atlantic City, NJ, May 1990, pp. 322–331.
P. BrodatzTextures: A Photographic Album for Artists and Designers, Dover: New York, 1966.
S.K.Chang, C.W.Yan, D.C.Dimitroff, and T.Arndt, “An intelligent image database system,” IEEE Transaction on Software Engineering, Vol. 14, No. 5, pp. 681–688, May 1988.
Y. Fisher, Fractal Imaging Compression: Theory and Applications, Springer-Verlag, 1995.
Y.Fisher, E.W.Jacobs, and R.D.Boss, “Iterated transformation image compression,” Technical Report TR-1408, Naval Oceans Systems Center, San Diego, CA, 1991.
Y. Fisher and A.F. Lawrance, “Fractal image compression for mass storage applications,” SPIE Image Storage and Retrieval Systems, 1662, 1992.
T. Gevers and A.W.M. Smeulders, An Approach to Image Retrieval for Image Databases, Vol. 720, Lecture Notes in Computer Science, Springer-Verlag: Berlin.
M. Gharavi-Alkhansari and T.S. Huang, “Fractal-based techniques for a generalized image coding method,” in Proceedings IEEE ICIP, 1994.
M.L.Hilton, B.D.Jawarth, and A.Sengupta, “Compressing still and moving images with wavelets,” Multimedia Systems, Vol. 2, No. 5, pp. 218–227, Dec. 1994.
T.-Y. Hou, P. Liu, A. Hsu, and M.-Y. Chiu, “Medical image retrieval by spatial features,” in IEEE Conference on Systems, Man, and Cybernetics, 1992.
A.E. Jacquin, “Image coding based on a fractal theory of iterated contractive image transformations,” IEEE Transactions on Image Processing, Vol. 1, No. 1, 1992.
A.E. Jacquin, “Fractal image coding: A review,” Proceedings of the IEEE, 1993, Vol. 81, No. 10.
S. Lepsqy, G.E. Olen, and T.A. Ramstad, “Attractor image compression with a fast non-iterative decoding algorithm,” in Proceedings of ICASSP-93, 1994.
K.-I.Lin, H.V.Jagadish and C.Faloutsos, “The TV-tree: An index structure for high-dimentional data,” The VLDB Journal, Vol. 3, No. 4, pp. 517–542, Oct. 1994.
W. Niblack, R. Barker, W. Equitz, M. Flickner, E. Glasman, D. Petkovic, P. Yanker, C. Faloutsos, and G. Taubin, “The qbic project: Querying images by content using color, texture, and shape,” Technical report, IBM Technical Report, 1993.
F. Rabitti and P. Savino, “Automatic image indexation and retrieval,” in Conference on Intelligent Text and Image Handling, 1991.
D. Saupe and R. Hamzaoui, “A guided tour of the fractal image compression literature,” in ACM SIGGRAPH'94 Course Notes, New Directions for Fractal Modelling in Computer Graphics, July 1994.
M.F.H. Sawhney, W. Niblack, J. Ashley, Q. Huang, B. Dom, M. Gorkani, J. Hafner, D. Lee, D. Petkovic, D. Steele, and P. Yanker, “Query by image and video content: The QBIC system,” IEEE Computer, Vol. 28, No. 9, 1995.
T. Sellis, N. Roussopoulos, and C. Faloutsos, “The R+-tree: A dynamic index of multidimensional objects,” in Proceedings of the 18th VLDB Conference, 1987.
A.D.Sloan, “Retrieving database contents by image recognition: New fractal power,” Advanced Imaging, Vol. 9, No. 5, pp. 26–30, 1994.
J.R. Smith and S. Chang, “Transform features for texture classification and discrimination in large image databases,” in Proceedings IEEE ICIP, 1994.
J.R. Smith and S.-F. Chang, “Quad-tree segmentation for texture-based image query,” in Proceedings of ACM Multimedia 94, San Francisco, California, Oct. 1994, pp. 279–286.
J.R. Smith and S.-F. Chang, “Automatic image retrieval using color and texture,” Technical report, Columbia University, 1995.
H. Tamura, S. Mori, and T. Yamawaki, “Texture features corresponding to visual perception,” IEEE Transactions on Systems, Man, and Cybernetics, Vol. 8, No. 6, June 1978.
A.Turtur, F.Prampolini, M.Fantini, R.Guarda, and M.A.Imperato, “IDB: An image database system,” IBM Journal of Research and Development, Vol. 35, No. 1, pp. 88–96, Jan. 1991.
C.J. van Rijsbergen, Retrieval Effectiveness, in K.S. Jones (Ed.), Information Retrieval Experiment, Butterworths, 1981, pp. 32–43.
G.K.Wallace, “The JPEG still picture compression standard,” Communications of the ACM, Vol. 34, No. 4, pp. 30–44, 1991.
J.K.Wu and A.D.Narasimhalu, “Identifying faces using multiple retrievals,” IEEE Multimedia, Vol. 1, No. 2, pp. 27–38, 1994.
A. Zhang, B. Cheng, and R. Acharya, “Texture-based image retrieval in image database systems,” in Proccedings of the Sixth International Workshop on Database and Expert Systems Applications (DEXA), London, United Kingdom, Sept. 1995. Invited paper.
A. Zhang, B. Cheng, and R. Acharya, “An approach to query-by-texture in image database systems,” in Proceedings of the SPIE Conference on Digital Image Storage and Archiving Systems, Philadelphia, Oct. 1995.
A. Zhang, B. Cheng, R. Acharya, and R. Menon, “Comparison of wavelet transforms and fractal coding in texture-based image retrieval,” in Proceedings of the SPIE Conference on Visual Data Exploration and Analysis III, San Jose, Jan. 1996.
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Zhang, A., Acharya, R. & Cheng, B. A fractal-based clustering approach in large visual database systems. Multimed Tools Appl 3, 225–244 (1996). https://doi.org/10.1007/BF00393939
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DOI: https://doi.org/10.1007/BF00393939