Bayesian decision versus voting for image retrieval
Image retrieval from image databases is usually performed by using global image characteristics. However the use of local image information is highly desirable when only part of the image is of interest. An original solution was introduced in  using invariant local signal characteristics. This paper extends this contribution by extending the set of invariants considered to allow illumination change. Then it is shown that the invariant distribution is far from uniform and a probabilistic indexing scheme is proposed. Experimental results validate the approch and the different methods are discussed.
Unable to display preview. Download preview PDF.
- 1.T.O. Binford and T.S. Levitt. Quasi-invariants: Theory and exploitation. In Proceedings of Darpa Image Understanding Workshop, pages 819–829, 1993.Google Scholar
- 2.J.B. Burns, R.S. Weiss, and E.M. Riseman. The non-existence of general-case view-invariants. In J.L. Mundy and A. Zisserman, editors, Geometric Invariance in Computer Vision, chapter 6, pages 120–131. The MIT Press, Cambridge, MA, USA, 1992.Google Scholar
- 3.L. Florack. The Syntactical Structure of Scalar Images. PhD thesis, Universiteit Utrecht, November 1993.Google Scholar
- 6.Romeny. Geometry-Driven Diffusion in Computer Vision. Kluwer Academic Publishers, 1994.Google Scholar
- 7.B. Schiele and J.L. Crowley. Object recognition using multidimensional receptive field histograms. In Proceedings of the 4th European Conference on Computer Vision, Cambridge, England, pages 610–619, 1996.Google Scholar
- 8.B. Schiele and J..L. Crowley. Probabilistic object recognition using multidimensional receptive field histogram. In Proceedings of the 13th International Conference on Pattern Recognition, Vienna, Austria, pages 50–54, 1996.Google Scholar
- 9.C. Schmid and R. Mohr. Combining greyvalue invariants with local constraints for object recognition. In Proceedings of the Conference on Computer Vision and Pattern Recognition, San Francisco, California, USA, June 1996. ftp://ftp.imag.fr/pub/MOVI/publications/Schmid_cvpr96.ps.z.Google Scholar
- 10.H. Schulz-Mirbach. Constructing invariant features by averaging techniques. In Proceedings of the 12th International Conference on Pattern Recognition, Jerusalem, Israel, pages 387–390, 1994.Google Scholar
- 12.M. Turk and A. Pentland. Face recognition using eigenfaces. In Proceedings of the Conference on Computer Vision and Pattern Recognition, Maui, Hawaii, USA, pages 586–591, 1991.Google Scholar