Efficient Graph-Based Image Segmentation
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This paper addresses the problem of segmenting an image into regions. We define a predicate for measuring the evidence for a boundary between two regions using a graph-based representation of the image. We then develop an efficient segmentation algorithm based on this predicate, and show that although this algorithm makes greedy decisions it produces segmentations that satisfy global properties. We apply the algorithm to image segmentation using two different kinds of local neighborhoods in constructing the graph, and illustrate the results with both real and synthetic images. The algorithm runs in time nearly linear in the number of graph edges and is also fast in practice. An important characteristic of the method is its ability to preserve detail in low-variability image regions while ignoring detail in high-variability regions.
- Arya, S. and Mount, D.M. 1993. Approximate nearest neighbor searching. In Proc. 4th Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 271–280.
- Ausiello, G., Crescenzi, P., Gambosi, G., Kann, V., Marchetti Spaccamela, A., and Protasi, M. (to appear). Complexity and Approximation. Combinatorial Optimization Problems and their Approximability Properties. Springer-Verlag: Berlin.
- Comaniciu, D. and Meer, P. 1997. Robust analysis of feature spaces: Color image segmentation. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 750–755.
- Comaniciu, D. and Meer, P. 1999. Mean shift analysis and applications. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1197–1203.
- Cooper, M.C. (1998) The tractability of segmentation and scene analysis. International Journal of Computer Vision 30: pp. 27-42
- Cormen, T.H., Leiserson, C.E., Rivest, R.L. (1990) Introduction to Algorithms. The MIT Press, McGraw-Hill Book Company
- Felzenszwalb, P. and Huttenlocher, D. 1998. Image segmentation using local variation. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 98–104.
- Gdalyahu, Y., Weinshall, D., and Werman, M. 1999. Stochastic clustering by typical cuts. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2596–2601.
- Jain, A.K. and Dubes, R.C. 1988. Algorithms for Clustering Data. Prentice Hall.
- Jermyn, I., Ishikawa, H. (2001) Globally optimal regions and boundaries as minimum ratio weight cycles. IEEE Transactions on Pattern Analysis and Machine Intelligence 23: pp. 1075-1088
- Pavlidas, T. 1977. Structural Pattern Recognition. Springer-Verlag.
- Perona, P. and Freeman, W. 1998. A factorization approach to grouping. In Proceedings of the European Conference on Computer Vision, pp. 655–670.
- Ratan, A.L., Maron, O., Grimson, W.E.L., and Lozano-Perez, T. 1999. A framework for learning query concepts in image classification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 423–431.
- Shi, J. and Malik, J. 1997. Normalized cuts and image segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 731–737.
- Urquhart, R. (1982) Graph theoretical clustering based on limited neighborhood sets. Pattern Recognition 15: pp. 173-187
- Weiss, Y. (1999) Segmentation using eigenvectors:Aunifying view. Proceedings of the International Conference on Computer Vision 2: pp. 975-982
- Wertheimer, M. 1938. Laws of organization in perceptual forms (partial translation). In A Sourcebook of Gestalt Psychology.W.B. Ellis (Ed.). Harcourt, Brace and Company, pp. 71–88.
- Wu, Z., Leahy, R. (1993) An optimal graph theoretic approach to data clustering: Theory and its application to image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 11: pp. 1101-1113
- Zahn, C.T. (1971) Graph-theoretic methods for detecting and describing gestalt clusters. IEEE Transactions on Computing 20: pp. 68-86
- Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Volume 59, Issue 2 , pp 167-181
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- Kluwer Academic Publishers
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- image segmentation
- perceptual organization
- graph algorithm
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