Shape Description for Content-Based Image Retrieval
The present work is focused on a global image characterization based on a description of the 2D displacements of the different shapes present in the image, which can be employed for CBIR applications. To this aim, a recognition system has been developed, that detects automatically image ROIs containing single objects, and classiffies them as belonging to a particular class of shapes. In our approach we make use of the eigenvalues of the covariance matrix computed from the pixel rows of a single ROI. These quantities are arranged in a vector form, and are classiffed using Support Vector Machines (SVMs). The selected feature allows us to recognize shapes in a robust fashion, despite rotations or scaling, and, to some extent, independently from the light conditions. Theoretical foundations of the approach are presented in the paper, together with an outline of the system, and some preliminary experimental results.
KeywordsImage Indexing Cumulative Function Video Database Optimal Hyperplane Shape Class
Unable to display preview. Download preview PDF.
- 2.S. Belongie, C. Carson, H. Greenspan, and J. Malik. Color-and Texture-based Image Segmentation using EM and its application to Content-based Image Rtrieval. In Proc. of International Conference on Computer Vision, 1998.Google Scholar
- 3.C. Carson, M. Thomas, S. Belongie, J.M. Hellerstein, and J. Malik. Blobworld: A System for Region-Based Image Indexing and Retrieval. In Proc. of Third International Conference on Visual Information Systems VISUAL’99, pages 509–516, Amsterdam, The Netherlands, June 1999. Springer.Google Scholar
- 4.M. La Cascia and E. Ardizzone. Jacob: Just a Content-based Query System for Video Databases. In Proc. of IEEE Int. Conference on Acoustics, Speech and Signal Processing, ICASSP-96, pages 7–10, Atlanta, May 1996.Google Scholar
- 5.M. Flickner, H. Sawhney, W. Niblack, J. Ashley, et al. Query by Image and Video Content: The QBIC System. IEEE Computer, 28(9):23–32, September 1995.Google Scholar
- 6.R.C. Gonzalez and P. Wintz. Digital Image Processing. Addison-Wesley, ii edition,1987.Google Scholar
- 8.Hampapur et al. Virage Video Engine. Proc. of SPIE, Storage and Retrieval for Image and Video Databeses V, 3022:188–200, 1997.Google Scholar
- 11.B. Schölkopf, C. Burges, and A.J. Smola, editors. Support Vector Learning. Advances in Kernel Methods. The MIT Press, Cambridge, MA, 1999.Google Scholar