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Image Database Design Based on 9D-SPA Representation for Spatial Relations
December 2004 (vol. 16 no. 12)
pp. 1486-1496
Spatial relationships between objects are important features for designing a content-based image retrieval system. In this paper, we propose a new scheme, called 9D-SPA representation, for encoding the spatial relations in an image. With this representation, important functions of intelligent image database systems such as visualization, browsing, spatial reasoning, iconic indexing, and similarity retrieval can be easily achieved. The capability of discriminating images based on 9D-SPA representation is much more powerful than any spatial representation method based on Minimum Bounding Rectangles or centroids of objects. The similarity measures using 9D-SPA representation provide a wide range of fuzzy matching capability in similarity retrieval to meet different user's requirements. Experimental results showed that our system is very effective in terms of recall and precision. In addition, the 9D-SPA representation can be incorporated into a two-level index structure to help reduce the search space of each query processing. The experimental results also demonstrated that, on average, only 0.1254 percent \sim 1.6829 percent of symbolic pictures (depending on various degrees of similarity) were accessed per query in an image database containing 50,000 symbolic pictures.

[1] J.R. Bach, C. Fuller, A. Gupta, A. Hampapur, B. Horowitz, R. Humphrey, R.C. Jain, and C. Shu, “Virage Image Search Engine: An Open Framework for Image Management,” Proc. Symp. Electronic Imaging: Science and Technology— Storage & Retrieval for Image and Video Database IV, pp. 76-87, 1996.
[2] B. Bhanu and S. Lee, Genetic Learning for Adaptive Image Segmentation. Norwell: Kluwer Academic, 1994.
[3] C.C. Chang, “Spatial Match Retrieval of Symbolic Pictures,” J. Information Science and Eng., vol. 7, pp. 405-422, Dec. 1991.
[4] S.K. Chang, E. Jungert, and Y. Li, “Representation and Retrieval of Symbolic Pictures Using Generalized 2D Strings,” technical report, Univ. of Pittsburg, 1988.
[5] 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. 413-428, May 1987.
[6] S.K. Chang, Principles of Pictorial Information Systems Design. Englewood Cliffs, N.J.: Prentice-Hall Inc., 1989.
[7] Y. Chen and J.Z. Wang, “A Region-Based Fuzzy Feature Matching Approach to Content-Based Image Retrieval,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 9, pp. 1252-1267, Sept. 2002.
[8] M. Flickner, H. Sawhney, W. Niblack, J. Ashley, Q. Huang, B. Dom, M. Gorkani, J. Hafner, D. Lee, D. Petkovic, D. Streele, and P. Yanker, “Query by Image and Video Content: The QBIC System,” Computer, vol. 28, no. 9, pp. 23-32, Sept. 1995.
[9] P.W. Huang and Y.R. Jean, “Using 2D ${\rm{PC^+{\hbox{-}}Strings}}$ as Spatial Knowledge Representation for Image Database Systems,” Pattern Recognition, vol. 27, no. 9, pp. 1249-1257, Sept. 1994.
[10] P.W. Huang and Y.R. Jean, “Design of Large Intelligent Image Database Systems,” Int'l J. Intelligent Systems, vol. 11, pp. 347-365, 1996.
[11] P.W. Huang and S.K. Dai, “Image Retrieval by Texture Similarity,” Pattern Recognition, vol. 36, pp. 665-679, 2003.
[12] E. Jungert, “Extended Symbolic Projections as a Knowledge Structure for Spatial Reasoning,” Proc. Fourth BPRA Conf. Pattern Recognition, pp. 343-351, 1988.
[13] E. Jungert and S.K. Chang, “An Algebra for Symbolic Image Manipulation and Transformation,” Visual Database Systems, T.L. Kunii, ed., North Holland: Elsevier Science Publishers B.V., 1989.
[14] L.J. Latecki and R. Lakamper, “Application of Planar Shape Comparison to Object Retrieval in Image Database,” Pattern Recognition, vol. 35, pp. 15-29, 2002.
[15] S.Y. Lee and F.J. Hsu, “2D C-String: A New Spatial Knowledge Representation for Image Database Systems,” Pattern Recognition, vol. 23, no. 10, pp. 1077-1087, Oct. 1990.
[16] S.Y. Lee and F.J. Hsu, “Spatial Reasoning and Similarity Retrieval of Images Using 2D C-String Knowledge Representation,” Pattern Recognition, vol. 25, no. 3, pp. 305-318, Mar. 1992.
[17] K.C. Liang and C.C. Jay Kuo, “WaveGuide: A Joint Wavelet-Based Image Representation and Description System,” IEEE Trans. Image Processing, vol. 8, no. 11, pp. 1619-1629, 1999.
[18] A.K. Majumdar, I. Bhattacharya, and A.K. Saha, “An Object-Oriented Fuzzy Data Model for Similarity Detection in Image Databases,” IEEE Trans. Knowledge and Data Eng., vol. 14, no. 5, pp. 1186-1189, Sept./Oct. 2002.
[19] M. Nabil, J. Shepherd, and A.H.H. Ngu, “2D Projection Interval Relationships: A Symbolic Representation of Spatial Relationships,” Lecture Notes in Computer Science, no. 951, pp. 292-309, 1995.
[20] M. Nabil, A.H.H. Ngu, and J. Shepherd, “Picture Similarity Retrieval Using the 2D Projection Interval Representation,” IEEE Trans. Knowledge and Data Eng., vol. 8, no. 4, pp. 533-539, Aug. 1996.
[21] A. Pentland, R.W. Picard, and S. Sclaroff, “Photobook: Tool for Content-Based Manipulation of Image Databases,” Int'l J. Computer Vision, vol. 18, no. 3, pp. 233-254, June 1996.
[22] G. Petraglia, M. Sebillo, M. Tucci, and G. Tortora, “Virtual Images for Similarity Retrieval in Image Databases,” IEEE Trans. Knowledge and Data Eng., vol. 13, no. 6, pp. 951-967, Nov./Dec. 2001.
[23] E. Petrakis, C. Faloutsos, and K.I. 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.
[24] A. Rao, R.K. Srihari, L. Zhu, and A. Zhang, “A Method for Measuring the Complexity of Image Databases,” IEEE Trans. Multimedia, vol. 4, no. 2, pp. 160-173, Mar./Apr. 2002.
[25] Y. Rui, T.S. Huang, “Image Retrieval: Current Techniques, Promising Directions, and Open Issues,” J. Visual Comm. Image Representation, vol. 10, pp. 39-62, 1999.
[26] J.R. Smith and S.F. Chang, “VisualSEEK: A Full Automated Content-Based Image Query System,” Proc. Fourth ACM Int'l Multimedia Conf., pp. 87-98, 1996.
[27] J. Vleugels, R.C. Veltkamp, and C. Remco, “Efficient Image Retrieval through Vantage Objects,” Pattern Recognition, vol. 35, pp. 69-80, 2002.
[28] X.M. Zhou and C.H. Ang, “Retrieving Similar Pictures from a Pictorial Database by an Improved Hashing Table,” Pattern Recognition Letters, vol. 18, pp. 751-758, 1997.
[29] , 2004.

Index Terms:
Image database, spatial relations, similarity retrieval, 9D-SPA, visualization.
Po-Whei Huang, Chu-Hui Lee, "Image Database Design Based on 9D-SPA Representation for Spatial Relations," IEEE Transactions on Knowledge and Data Engineering, vol. 16, no. 12, pp. 1486-1496, Dec. 2004, doi:10.1109/TKDE.2004.92
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