Abstract
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 [9] 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.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
T.O. Binford and T.S. Levitt. Quasi-invariants: Theory and exploitation. In Proceedings of Darpa Image Understanding Workshop, pages 819–829, 1993.
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.
L. Florack. The Syntactical Structure of Scalar Images. PhD thesis, Universiteit Utrecht, November 1993.
J.J. Koenderink and A.J. van Doorn. Representation of local geometry in the visual system. Biological Cybernetics, 55:367–375, 1987.
A. Pentland, R.W. Picard, and S. Sclaroff. Photobook: Content-based manipulation of image databases. International Journal of Computer Vision, 18(3):233–254, 1996.
Romeny. Geometry-Driven Diffusion in Computer Vision. Kluwer Academic Publishers, 1994.
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.
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.
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.
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.
M.J. Swain and D.H. Ballard. Color indexing. International Journal of Computer Vision, 7(1):11–32, 1991.
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.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1997 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mohr, R., Picard, S., Schmid, C. (1997). Bayesian decision versus voting for image retrieval. In: Sommer, G., Daniilidis, K., Pauli, J. (eds) Computer Analysis of Images and Patterns. CAIP 1997. Lecture Notes in Computer Science, vol 1296. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63460-6_140
Download citation
DOI: https://doi.org/10.1007/3-540-63460-6_140
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-63460-7
Online ISBN: 978-3-540-69556-1
eBook Packages: Springer Book Archive