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Geometry-Based Image Retrieval in Binary Image Databases
June 2008 (vol. 30 no. 6)
pp. 1003-1013
In this paper, a geometry-based image retrieval system is developed for multi-object images. We model both shape and topology of image objects using a structured representation called curvature tree (CT). The hierarchy of the CT reflects the inclusion relationships between the image objects. To facilitate shape-based matching, triangle-area representation (TAR) of each object is stored at the corresponding node in the CT. The similarity between two multi-object images is measured based on the maximum similarity subtree isomorphism (MSSI) between their CTs. For this purpose, we adopt a recursive algorithm to solve the MSSI problem and a very effective dynamic programming algorithm to measure the similarity between the attributed nodes. Our matching scheme agrees with many recent findings in psychology about the human perception of multi-object images. Experiments on a database of 13500 real and synthesized medical images and the MPEG-7 CE-1 database of 1400 shape images have shown the effectiveness of the proposed method.

[1] T. Adamek and N.E. O'Connor, “A Multiscale Representation Method for Nonrigid Shapes with a Single Closed Contour,” IEEE Trans. Circuits and Systems for Video Technology, vol. 14, no. 5, pp.742-753, May 2004.
[2] N. Alajlan, I. Elrube, M.S. Kamel, and G. Freeman, “Shape Retrieval Using Triangle-Area Representation and Dynamic Space Warping,” technical report, PAMI Group, Univ. of Waterloo, http://pami.uwaterloo.canav.php?site=pub&action=list&researcher= naif , Aug. 2006.
[3] J. Bach, C. Fuller, A. Gupta, A. Hampapur, B. Horowitz, R. Humphrey, R. Jain, and C. Shu, “Virage Image Search Engine: An Open Framework for Image Management,” Storage and Retrieval for Image and Video Databases, pp. 76-87, Feb. 1996.
[4] O. El Badawy and M.S. Kamel, “Shape Representation Using Concavity Graphs,” Proc. 16th Int'l Conf. Pattern Recognition, vol. 3, pp. 461-464, 2002.
[5] S. Belongie, J. Malik, and J. Puzicha, “Shape Matching and Object Recognition Using Shape Contexts,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 4, pp. 509-522, Apr. 2002.
[6] I. Biederman, “Recognition by Components: A Theory of Human Image Understanding,” Psychological Rev., vol. 94, no. 2, pp. 115-147, 1987.
[7] C.C. Chang and S.Y. Lee, “Retrieval of Similar Pictures on Pictorial Databases,” Pattern Recognition, vol. 24, no. 7, pp. 675-681, 1991.
[8] S.K. Chang, Q.Y. Shi, and C.W. Yan, “Iconic Indexing by 2D Strings,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 9, no. 3, pp. 413-428, 1987.
[9] J.P. Eakins, “Towards Intelligent Image Retrieval,” Pattern Recognition, vol. 35, no. 1, pp. 3-14, 2002.
[10] J.P. Eakins, K.J. Riley, and J.D. Edwards, “Shape Feature Matching for Trademark Image Retrieval,” Proc. Int'l Conf. Image and Video Retrieval, vol. 2728, pp. 28-37, Aug. 2003.
[11] J.P. Eakins, K. Shields, and J.M. Boardman, “Artisan: A Shape Retrieval System Based on Boundary Family Indexing,” Proc. Storage and Retrieval for Still Image and Video Databases, vol. 2670, pp. 17-28, 1996.
[12] E.A. El-Kwae and M.R. Kabuka, “A Robust Framework for Content-Based Retrieval by Spatial Similarity in Image Databases,” ACM Trans. Information Systems, vol. 17, no. 2, pp. 174-198, 1999.
[13] C. Faloutsos, R. Barber, M. Flickner, J. Hafner, W. Niblack, D. Petkovic, and W. Equitz, “Efficient and Effective Querying by Image Content,” J. Intelligent Information Systems, vol. 3, nos. 3/4, pp. 231-262, 1994.
[14] 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, vol. 28, no. 9, pp. 23-32, Sept. 1995.
[15] V. Gudivada and V. Raghavan, “Design and Evaluation of Algorithms for Image Retrieval by Spatial Similarity,” ACM Trans. Information Systems, vol. 13, no. 2, pp. 115-144, 1995.
[16] F. Harary, Graph Theory. Addison-Wesley, 1969.
[17] M.K. Hu, “Visual Pattern Recognition by Moment Invariants,” IRE Trans. Information Theory, vol. 8, pp. 179-197, 1962.
[18] A.K. Jain and A. Vailaya, “Shape-Based Retrieval: A Case Study with Trademark Image Databases,” Pattern Recognition, vol. 31, no. 9, pp. 1369-1390, 1998.
[19] A. Khotanzad and Y.H. Hong, “Invariant Image Recognition by Zernike Moments,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 12, no. 5, pp. 489-497, May 1990.
[20] W.Y. Kim and Y.S. Kim, “A New Region-Based Shape Descriptor,” Technical Report ISO/IEC MPEG99/M5472, Dec. 1999.
[21] Y.S. Kim and W.Y. Kim, “Content-Based Trademark Retrieval System Using a Visually Salient Feature,” Image and Vision Computing, vol. 16, nos. 12-13, pp. 931-939, Aug. 1998.
[22] L.J. Latecki, R. Lakamper, and U. Eckhardt, “Shape Descriptors for Non-Rigid Shapes with a Single Closed Contour,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 424-429, 2000.
[23] H. Ling and D. Jacobs, “Using the Inner Distance for Classification of Articulated Shapes,” Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 719-726, June 2005.
[24] S. Loncaric, “A Survey of Shape Analysis Techniques,” Pattern Recognition, vol. 31, no. 8, pp. 983-1001, 1998.
[25] D.G. Lowe, Perceptual Organization and Visual Recognition. Kluwer Academic, 1985.
[26] A. Markman and D. Gentner, “Structure Mapping in the Comparison Process,” Am. J. Psychology, vol. 113, no. 4, pp. 501-538, 2000.
[27] F. Mokhtarian and M. Bober, Curvature Scale Space Representation: Theory, Applications, and MPEG-7 Standardization. Kluwer Academic, 2003.
[28] F. Mokhtarian and A. Mackworth, “Scale-Based Description and Recognition of Planar Curves and Two-Dimensional Shapes,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 8, no. 1, pp. 34-43, 1986.
[29] P. Monasse and F. Guichard, “Fast Computation of a Contrast-Invariant Image Representation,” IEEE Trans. Image Processing, vol. 9, no. 5, pp. 860-872, 2000.
[30] P. Monasse and F. Guichard, “Scale-Space from a Level Lines Tree,” J. Visual Communication and Image Representation, vol. 11, no. 2, pp. 224-236, 2000.
[31] J. Munkres, “Algorithms for Assignment and Transportation Problems,” J. Soc. Industrial and Applied Math., vol. 5, no. 1, pp.32-38, 1957.
[32] The MPEG Home Page, http://www.chiariglione.orgmpeg, 2008.
[33] C. Papadimitriou and K. Steiglitz, Combinatorial Optimization: Algorithms and Complexity. Prentice Hall, 1982.
[34] H.L. Peng and S.Y. Chen, “Trademark Shape Recognition Using Closed Contours,” Pattern Recognition Letters, vol. 18, no. 8, pp.791-803, Aug. 1997.
[35] A. Pentland, R. Picard, and S. Sclaroff, “Photobook: Content-Based Manipulation of Image Databases,” Int'l J. Computer Vision, vol. 18, no. 3, pp. 233-254, 1996.
[36] E.G.M. Petrakis, “Design and Evaluation of Spatial Similarity Approaches for Image Retrieval,” Image and Vision Computing, vol. 20, no. 1, pp. 59-76, Jan. 2002.
[37] E.G.M. Petrakis and C. Faloutsos, “Similarity Searching in Medical Image Databases,” IEEE Trans. Knowledge and Data Eng., vol. 9, no. 3, pp. 435-447, May/June 1997.
[38] E.G.M. Petrakis, C. Faloutsos, and K. Lin, “Imagemap: An Image Indexing Method Based on Spatial Similarity,” IEEE Trans. Knowledge and Data Eng., vol. 14, no. 5, pp. 979-987, Sept./Oct. 2002.
[39] P. Salembier and L. Garrido, “Binary Partition Tree as an Efficient Representation for Image Processing, Segmentation, and Information Retrieval,” IEEE Trans. Image Processing, vol. 9, no. 4, pp. 561-576, 2000.
[40] A.W.M. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain, “Content-Based Image Retrieval at the End of the Early Years,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 12, pp. 1349-1380, Dec. 2000.
[41] A. Soffer and H. Samet, “Using Negative Shape Features for Logo Similarity Matching,” Proc. 14th Int'l Conf. Pattern Recognition, vol. 1, pp. 571-573, Aug. 1998.
[42] K. Thorisson, “Simulated Perceptual Grouping: An Application to Human Computer Interaction,” Proc. 16th Ann. Conf. Cognitive Science Soc., pp. 876-881, Aug. 1994.
[43] A. Torsello, D. Hidovic, and M. Pelillo, “Polynomial-Time Metrics for Attributed Trees,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 7, pp. 1087-1099, July 2005.
[44] R. Veltkamp and M. Hagedoorn, “State-of-the-Art in Shape Matching,” Principles of Visual Information Retrieval, Springer, pp. 87-119, 2001.
[45] R. Veltkamp and M. Tanase, “Content-Based Image Retrieval Systems: A Survey,” Technical Report UU-CS-2000-34, Inst. ICS, Utrecht Univ., 2000.
[46] NIST Special Publication 500-242: The Seventh Text REtrieval Conf., E.M. Voorhees and D.K. Harmann, eds., Nat'l Inst. Standards and Tech nology, 1998.
[47] D. Zhang and G. Lu, “Review of Shape Representation and Description Techniques,” Pattern Recognition, vol. 37, no. 1, pp. 1-19, 2004.

Index Terms:
Shape, Size and shape, Hierarchical
Naif Alajlan, Mohamed S. Kamel, George H. Freeman, "Geometry-Based Image Retrieval in Binary Image Databases," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 6, pp. 1003-1013, June 2008, doi:10.1109/TPAMI.2008.37
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