Abstract
This paper presents a quantitative approach to grouping. A generic grouping method, which may be applied to many domains, is given, and an analysis of its expected grouping quality is done. The grouping method is divided into two parts: Constructing a graph representation of the geometric relations in the data set, and then finding the “best” partition of the graph into groups. Both stages are implemented using known statistical tools such as Wald's SPRT algorithm and the Maximum Likelihood criterion. The accompanying quantitative analysis shows some relations between the data quality, the reliability of the grouping cues and the computational efforts, to the expected grouping quality. To our best knowledge, such an analysis of a grouping process is given here for the first time. The synthesis of specific grouping algorithms is demonstrated for three different grouping tasks and domains. Experimental results show the ability of this generic approach to provide successful algorithm in specific domains.
Chapter PDF
Keywords
References
Adelson, E. H., and Wang, J. Y. A. Representing moving images with layers. Tech. Rep. 279, M.I.T, May 1994.
Amir, A., and Lindenbaum, M. The construction and analysis of a generic grouping algorithm. Tech. Rep. CIS-9418, Technion, Israel, Nov. 1994.
Guy, G., and Medioni, G., Perceptual grouping using global saliency-enhancing operators. In ICPR-92 (1992), vol. I, pp. 99–103.
Herault, L., and Horaud, R., Figure-ground discrimination: A combinatorial optimization approach. PAMI 15, 9 (Sep 1993), 899–914.
Jacobs, D. W., and Chennubhotla, C. Finding structurally consistent motion correspondences. In ICPR-94, Jerusalem (1994), pp. 650–653.
Lowe, D. G. Perceptual Organization and Visual Recognition. Kluwer Academic Pub., 1985.
Sha'ashua, A., and Ullman, S. Grouping contours by iterated pairing network. Neural Information Processing Systems (NIPS) 3 (1990).
Shapiro, L. G., and Haralick, R. M., Decomposition of two-dimentional shapes by graph-theoretic clustering. PAMI 1, 1 (Jan. 1979), 10–20.
Vosselman, G. Relational Matching, third ed. Lect. Notes in CS. Springer, 1992.
Wald, A. Sequencial Analysis, third ed. Wiley Publications in Statistics. 1947(1952).
Wu, Z., and Leahy, R., An optimal graph theoretic approach to data clustering: Theory and its application to image segmentation. PAMI 15, 11 (Nov 1993), 1001–1113.
Zisserman, A., Mundy, J., Forsyth, D., and Liu, J. Class-based grouping in perspective images. In ICCV-95, MIT (1995), pp. 183–188.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1996 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Amir, A., Lindenbaum, M. (1996). Quantitative analysis of grouping processes. In: Buxton, B., Cipolla, R. (eds) Computer Vision — ECCV '96. ECCV 1996. Lecture Notes in Computer Science, vol 1064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0015551
Download citation
DOI: https://doi.org/10.1007/BFb0015551
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-61122-6
Online ISBN: 978-3-540-49949-7
eBook Packages: Springer Book Archive