In this paper, we investigate approaches to supporting effective and efficient retrieval of image data based on content. We firstintroduce an effective block-oriented image decomposition structure which can be used to represent image content inimage database systems. We then discuss theapplication of this image data model to content-based image retrieval.Using wavelet transforms to extract image features,significant content features can be extracted from image datathrough decorrelating the data in their pixel format into frequency domain. Feature vectors ofimages can then be constructed. Content-based image retrievalis performed by comparing the feature vectors of the query imageand the decomposed segments in database images.Our experimental analysis illustrates that the proposed block-oriented image representationoffers a novel decomposition structure to be used tofacilitate effective and efficient image retrieval.
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M. Arya, W. Cody, C. Faloutsos, J. Richardson, and A. TogaQBISM: A Prototype 3-D Medical Image Database System,” IEEE Data Engineering Bulletin, Vol. 16, pp. 38–42, 1993.
J.R. Bach, S. Paul, and R. JainA Visual Information Management System for the Interactive Retrieval of Faces,” IEEE Transactions on Knowledge and Data Engineering, Vol. 5, pp. 619–628, 1993.
P. Brodatz, Textures: A Photographic Album for Artists and Designers, Dover, New York, 1966.
S.K. Chang, Principles of Pictorial Information Systems Design, Prentice Hall, 1989.
S.K. Chang, C.W. Yan, Donald C. Dimitroff, and Timothy Arndt, An Intelligent Image Database System, IEEE Transaction on Software Engineering, Vol. 14, pp. 681–688, 1988.
T.-Y. Hou, P. Liu, A. Hsu, and M.-Y. ChiuMedical Image Retrieval by Spatial Features,” in IEEE Conference on Systems, Man, and Cybernetics, 1992.
K.-I. Lin, H.V. Jagadish, and C. FaloutsosThe TV-Tree: an Index Structure for High-Dimentional Data,” The VLDB Journal, Vol. 3, pp. 517–542, 1994.
S. MallatMultiresolution approximation and wavelet orthonormal bases of l 2(r),” Transactions of American Mathematical Society, Vol. 315, pp. 69–87, 1989.
S. MallatA theory for multiresolution signal decomposition: the wavelet representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 11, pp. 674–693, 1989.
F. Rabitti and P. SavinoAutomatic Image Indexation and Retrieval,” in Conference on Intelligent Text and Image Handling, 1991.
John R. Smith and Shih-Fu ChangQuad-Tree Segmentation for Texture-Based Image Query,” in Proc. of ACM Multimedia 94, San Francisco, California, October 1994, pp. 279–286.
T.F. Syeda-MahmoodAttentional Selection in Object Recognition,” PhD thesis, Department of Computer Science, MIT, May 1993.
T.E. Syeda-MahmoodModel-driven Selection Using Texture,” in J. Illingworth, editor, Proceedings of the British Machine Conference, 1993, pp. 65–74.
G. Strang and T. Nguyen, Wavelets and Filter Banks, Wellesley-Cambridge Press, 1996.
A. Turtur, F. Prampolini, M. Fantini, R. Guarda, and M.A. ImperatoIDB: An Image Database System, IBM Journal of Research and Development, Vol. 35, pp. 88–96, 1991.
P. P. VaidyanathanMultirate Systems And Filter Banks,” Prentice Hall Signal Processing Series, Prentice Hall, Englewood Cliffs, NJ, 1993.
C. J. van RijsbergenRetrieval Effectiveness,” in Karen Sparck Jones, editor, Information Retrieval Experiment, pp. 32–43. Butterworths, 1981.
J. K. Wu and A. D. NarasimhaluIdentifying Faces Using Multiple Retrievals,” IEEE Multimedia, Vol. 1, pp. 27–38, 1994.
A. Zhang, B. Cheng, and R. AcharyaAn Approach to Query-by-texture in Image Database Systems,” in Proceedings of the SPIE Conference on Digital Image Storage and Archiving Systems, Philadelphia, October 1995, pp. 338–349.
A. Zhang, B. Cheng, and R. AcharyaA Fractal-Based Clustering Approach in Large Visual Database Systems,” The International Journal on Multimedia Tools and Applications, 1996, (to appear).
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Remias, E., Sheikholeslami, G., Zhang, A. et al. Supporting Content-Based Retrieval in Large Image Database Systems. Multimedia Tools and Applications 4, 153–170 (1997). https://doi.org/10.1023/A:1009614331352
- content-based image retrieval
- image database systems
- image decomposition
- image representation
- wavelet transforms