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Content-Based Image Retrieval Using Shape and Depth from an Engineering Database

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Advances in Visual Computing (ISVC 2007)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4842))

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Abstract

Content based image retrieval (CBIR), a technique which uses visual contents to search images from the large scale image databases, is an active area of research for the past decade. It is increasingly evident that an image retrieval system has to be domain specific. In this paper, we present an algorithm for retrieving images with respect to a database consisting of engineering/computer-aided design (CAD) models. The algorithm uses the shape information in an image along with its 3D information. A linear approximation procedure that can capture the depth information using the idea of shape from shading has been used. Retrieval of objects is then done using a similarity measure that combines shape and the depth information. Plotted precision/recall curves show that this method is very effective for an engineering database.

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References

  1. Huang, P., Jean, Y.: Using 2d c+-strings as spatial knowledge representation for image database systems 27, 1249–1257 (1994)

    Google Scholar 

  2. Huang, J., Kumar, S.R., Mitra, M., Zhu, W.J., Zabih, R.: Spatial color indexing and applications. Int. J. Comput. Vision 35, 245–268 (1999)

    Article  Google Scholar 

  3. Jain, A., Vailaya, A.: Image retrieval using color and shape. Pattern Recognition 29, 1233–1244 (1996)

    Article  Google Scholar 

  4. Saykol, E., Gudukbay, U., Ulusoy, O.: A histogram-based approach for object-based query-by-shape-and-color in multimedia databases. Technical Report BU-CE-0201, Bilkent University, Computer Engineering Dept (2002)

    Google Scholar 

  5. Caputo, B., Dorko, G.: How to combine color and shape information for 3d object recognition: kernels do the trick (2002)

    Google Scholar 

  6. Diplaros, A., Gevers, T., Patras, I.: Combining color and shape information for illumination-viewpoint invariant object recognition 15, 1–11 (2006)

    Google Scholar 

  7. Pala, S.: Image retrieval by shape and texture. PATREC: Pattern Recognition. Pergamon Press 32 (1999)

    Google Scholar 

  8. Smith, J.R., Chang, S.F.: Automated image retrieval using color and texture. Technical Report 414-95-20, Columbia University, Department of Electrical Engineering and Center for Telecommunications Research (1995)

    Google Scholar 

  9. Li, X., Chen, S.C., Shyu, M.L., Furht, B.: Image retrieval by color, texture, and spatial information. In: Proceedings of the the 8th International Conference on Distributed Multimedia Systems (DMS 2002), San Francisco Bay, CA, USA, pp. 152–159 (2002)

    Google Scholar 

  10. Carson, C., Thomas, M., Belongie, S., Hellerstein, J.M., Malik, J.: Blobworld: A system for region-based image indexing and retrieval. In: Third International Conference on Visual Information Systems, Springer, Heidelberg (1999)

    Google Scholar 

  11. Shao, L., Brady, M.: Invariant salient regions based image retrieval under viewpoint and illumination variations. J. Vis. Comun. Image Represent. 17, 1256–1272 (2006)

    Article  Google Scholar 

  12. Veltkamp, R., Tanase, M.: Content-based image retrieval systems: A survey. Technical Report UU-CS-2000-34, Utrecht University, Department of Computer Science (2000)

    Google Scholar 

  13. Datta, R., Li, J., Wang, J.Z.: Content-based image retrieval: approaches and trends of the new age. In: MIR 2005: Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval, pp. 253–262. ACM Press, New York (2005)

    Chapter  Google Scholar 

  14. Chang, H., Yeung, D.Y.: Kernel-based distance metric learning for content-based image retrieval. Image Vision Comput. 25, 695–703 (2007)

    Article  Google Scholar 

  15. Cz´uni, L., Csord´as, D.: Depth-based indexing and retrieval of photographic images. In: García, N., Salgado, L., Martínez, J.M. (eds.) VLBV 2003. LNCS, vol. 2849, pp. 76–83. Springer, Heidelberg (2003)

    Google Scholar 

  16. Zhang, D.S., Lu, G.: A comparative study on shape retrieval using fourier descriptors with different shape signatures. In: Proc. of International Conference on Intelligent Multimedia and Distance Education (ICIMADE 2001), Fargo, ND, USA, pp. 1–9 (2001)

    Google Scholar 

  17. Tsai, P., Shah, M.: Shape from shading using linear-approximation. IVC 12, 487–498 (1994)

    Google Scholar 

  18. Lee, K.M., Kuo, C.C.J.: Shape from shading with a generalized reflectance map model. Comput. Vis. Image Underst. 67, 143–160 (1997)

    Article  Google Scholar 

  19. Jayanti, S., Kalyanaraman, Y., Iyer, N., Ramani, K.: Developing an engineering shape benchmark for cad models. Computer-Aided Design 38, 939–953 (2006)

    Article  Google Scholar 

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George Bebis Richard Boyle Bahram Parvin Darko Koracin Nikos Paragios Syeda-Mahmood Tanveer Tao Ju Zicheng Liu Sabine Coquillart Carolina Cruz-Neira Torsten Müller Tom Malzbender

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Jain, A., Muthuganapathy, R., Ramani, K. (2007). Content-Based Image Retrieval Using Shape and Depth from an Engineering Database. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2007. Lecture Notes in Computer Science, vol 4842. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76856-2_25

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  • DOI: https://doi.org/10.1007/978-3-540-76856-2_25

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-76855-5

  • Online ISBN: 978-3-540-76856-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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