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ΘR$\Re$-String: A Geometry-Based Representation for Efficient and Effective Retrieval of Images by Spatial Similarity
May/June 1998 (vol. 10 no. 3)
pp. 504-512

Abstract—A spatial similarity algorithm assesses the degree to which the spatial relationships among the domain objects in a database image conform to those specified in the query image. In this paper, we propose a geometry-based structure for representing the spatial relationships in the images and an associated spatial similarity algorithm. The proposed algorithm recognizes both translation, scale, and rotation variants of an image, and variants of the image generated by an arbitrary composition of translation, scale, and rotation transformations. The algorithm has Θ(n log n) time complexity in terms of the number of objects common to the database and query images. The retrieval effectiveness of the proposed algorithm is evaluated using the TESSA image collection.

[1] T. Arndt and S.K. Chang,“Image sequence compression by iconic indexing,” IEEE VL’89 Workshop on Visual Languages, pp. 177-182,Roma, Italy, Sept. 1989.
[2] D.H. Ballard and C.M. Brown, Computer Vision, Prentice Hall, Upper Saddle River, N.J., 1982.
[3] C.C. Chang and S.Y. Lee, “Retrieval of Similar Pictures on Pictorial Databases,” Pattern Recognition, vol. 24, no. 7, pp. 675–680, 1991.
[4] S.K. Chang, E. Jungert, and Y. Li, "The Design of Pictorial Database Based Upon the Theory of Symbolic Projections," Proc. Conf. Very Large Spatial Databases, Springer-Verlag, 1989.
[5] S.K. Chang, C. Lee, and C. Dow, "A 2D String Matching Algorithm for Conceptual Pictorial Queries," Image Storage and Retrieval Systems, pp. 47-58, SPIE, vol. 1,662, 1992.
[6] S.K. Chang, Q.Y. Shi, and C.W. Yan, “Iconic Indexing by 2-D Strings,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 9, no. 3, pp. 413-427, July 1987.
[7] G. Costagliola, G. Tortora, and T. Arndt, “A Unifying Approach to Iconic Indexing for 2-D and 3-D Scenes,” IEEE Trans. Knowledge and Data Eng., vol. 4, no. 3, pp. 205-222, June 1992.
[8] D. Daneels et al., "Interactive Outlining: An Improved Approach Using Active Contours," Storage and Retrieval for Image and Video Databases, pp. 226-233, SPIE, vol. 1,908, 1993.
[9] M. Flickner, 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, 1995.
[10] W.B. Frakes and R. Baeza-Yates, Information Retrieval Data Structures&Algorithmss.Englewood Cliffs, N.J.: Prentice Hall, 1992.
[11] V. Gudivada, "TESSA—An Image Testbed for Evaluating 2D Spatial Similarity Algorithms," ACM SIGIR Forum, vol. 28, no. 2, pp. 17-36, 1994.
[12] Computer, special issue on content-based image retrieval systems, V. Gudivada and V. Raghavan, guest eds., vol. 28, no. 9, 1995.
[13] V.N. Gudivada, "Spatial Knowledge Representation and Retreival in 3D Image Databases," Int'l Conf. Multimedia and Computing Systems, IEEE CS Press, Los Alamitos, Calif., May, 1995, pp. 90-98.
[14] V.N. Gudivada and V.V. Raghavan, “Design and Evaluation of Algorithms for Image Retrieval by Spatial Similarity,” ACM Trans. Information Systems, vol. 13, no. 2, Apr. 1995.
[15] V.N. Gudivada and V.V. Raghavan, "Modeling and Retrieving Images by Content," Information Processing and Management, vol. 33, no. 4, pp. 427-452, 1997.
[16] V. Gudivada, R. Sonak, W. Grosky, and K. Lentz, "Indexing for Efficient Spatial Similarity-Retrieval in Multimedia Databases," B. Furht, ed., Handbook of Multimedia Systems, CRC Press 1998.
[17] T.-Y. Hou et al., "A Content-Based Indexing Technique Using Relative Geometry Features," Storage and Retrieval for Image and Video Databases, pp. 59-68. SPIE, vol. 1,662, 1992.
[18] Y. Lamdan and H.J. Wolfson, "Geometric hashing: A general and efficient model-based recognition scheme," Second Int'l Conf. Computer Vision, pp. 238-249, 1988.
[19] S.Y. Lee and F.J. Hsu, “2D C-String: A New Spatial Knowledge Representation for Image Database Systems,” Pattern Recognition, vol. 23, pp. 1077-1088, Oct. 1990.
[20] S.Y. Lee and F. Hsu, “Spatial Reasoning and Similarity Retrieval of Images using 2D C-Strings Knowledge Representation,” Pattern Recognition, vol. 25, no. 3, pp. 305-318, 1992.
[21] S.Y. Lee, M.K. Shan, and W.P. Yang, “Similarity Retrieval of Iconic Image Database,” Pattern Recognition, vol. 22, no. 6, pp. 675-682, 1989.
[22] A. Pentland, R. Picard, and S. Sclaroff, "Photobook: Tools for Content-Based Manipulation of Image Databases," Storage and Retrieval for Image and Video Databases II, SPIE, vol. 2,185, 1994.
[23] A.P. Sistla, C.T. Yu, and R. Haddad, “Reasoning About Spatial Relationships in Picture Retrieval Systems, Proc. 1994 Int’l Conf. Very Large Databases, Morgan Kaufmann, San Mateo, Calif., 1994.
[24] H. Wolfson, "Model Based Object Recognition by Geometric Hashing," Proc. First Europe Conf. Computer Vision, pp. 526-536, 1990.

Index Terms:
Image representation, matching, multimedia databases, spatial similarity, retrieval algorithms.
Venkat N. Gudivada, "ΘR$\Re$-String: A Geometry-Based Representation for Efficient and Effective Retrieval of Images by Spatial Similarity," IEEE Transactions on Knowledge and Data Engineering, vol. 10, no. 3, pp. 504-512, May-June 1998, doi:10.1109/69.687982
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