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Efficient Graph-Based Image Segmentation

Felzenszwalb, Pedro F., Huttenlocher, Daniel P.·2004·International Journal of Computer Vision

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Research works printed but not linked: 19

  1. Arya, S. and Mount, D.M. 1993. Approximate nearest neighbor searching. In Proc. 4th Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 271–280.
  2. 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.
  3. 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. · DOI
  4. 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. · DOI · Paper page
  5. Cooper, M.C. 1998. The tractability of segmentation and scene analysis. International Journal of Computer Vision, 30(1):27–42. · DOI
  6. Cormen, T.H., Leiserson, C.E., and Rivest, R.L. 1990. Introduction to Algorithms. The MIT Press: McGraw-Hill Book Company.
  7. 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. · DOI
  8. 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.
  9. Jain, A.K. and Dubes, R.C. 1988. Algorithms for Clustering Data. Prentice Hall.
  10. Jermyn, I. and Ishikawa, H. 2001. Globally optimal regions and boundaries as minimum ratio weight cycles. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23:1075–1088. · DOI
  11. Pavlidas, T. 1977. Structural Pattern Recognition. Springer-Verlag. · DOI · Paper page
  12. Perona, P. and Freeman, W. 1998. A factorization approach to grouping. In Proceedings of the European Conference on Computer Vision, pp. 655–670. · DOI · Paper page
  13. 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. · DOI
  14. 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.
  15. Urquhart, R. 1982. Graph theoretical clustering based on limited neighborhood sets. Pattern Recognition, 15(3):173–187. · DOI
  16. Weiss, Y. 1999. Segmentation using eigenvectors:Aunifying view. In Proceedings of the International Conference on Computer Vision, 2:975–982.
  17. 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. · DOI · Paper page
  18. Wu, Z. and 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, 11:1101–1113. · DOI · Paper page
  19. Zahn, C.T. 1971. Graph-theoretic methods for detecting and describing gestalt clusters. IEEE Transactions on Computing, 20:68–86. · DOI · Paper page

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Metadata

Authors
Felzenszwalb, P. F., & Huttenlocher, D. P.
Venue
International Journal of Computer Vision
Published
2004-09
Volume
59
Issue
2
Pages
167-181
ISSN
  • 0920-5691 (ISSN-L)
  • 1573-1405
OpenAlex
W1999478155
Cited by
6,176
References
0
FWCI
48.57
Type
journal article
Language
en
Open access
closed
Licence

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CoChat. (2026). Citation check: Efficient Graph-Based Image Segmentation. https://app.cochat.ai/papers/10.1023/b:visi.0000022288.19776.77
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Felzenszwalb, P. F., & Huttenlocher, D. P. (2004). Efficient Graph-Based Image Segmentation. International Journal of Computer Vision. https://doi.org/10.1023/b%3Avisi.0000022288.19776.77

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.