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Hierarchy and adaptivity in segmenting visual scenes

Author

Listed:
  • Eitan Sharon

    (The Weizmann Institute of Science)

  • Meirav Galun

    (The Weizmann Institute of Science)

  • Dahlia Sharon

    (Massachusetts General Hospital)

  • Ronen Basri

    (The Weizmann Institute of Science)

  • Achi Brandt

    (The Weizmann Institute of Science)

Abstract

Seeing things Humans usually can effortlessly find coherent regions even in noisy visual images, a task that is crucial for object recognition. Computer algorithms have been less successful at doing this in natural viewing conditions, in part because early work on the problem used only local computations on the image. Now a new approach has been developed, based on an image segmentation strategy that analyses all salient regions of an image and builds them into a hierarchical structure. This method is faster and more accurate than previous approaches, but the resulting algorithm is relatively simple to use. It is demonstrated in action by using it to find items within a large database of objects that match a target item.

Suggested Citation

  • Eitan Sharon & Meirav Galun & Dahlia Sharon & Ronen Basri & Achi Brandt, 2006. "Hierarchy and adaptivity in segmenting visual scenes," Nature, Nature, vol. 442(7104), pages 810-813, August.
    • Handle: RePEc:nat:nature:v:442:y:2006:i:7104:d:10.1038_nature04977
    DOI: 10.1038/nature04977
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    Cited by:

    1. Caraballo, Luis Evaristo & Díaz-Báñez, José-Miguel & Kroher, Nadine, 2021. "A polynomial algorithm for balanced clustering via graph partitioning," European Journal of Operational Research, Elsevier, vol. 289(2), pages 456-469.
    2. Yan T. Yang & Barak Fishbain & Dorit S. Hochbaum & Eric B. Norman & Erik Swanberg, 2014. "The Supervised Normalized Cut Method for Detecting, Classifying, and Identifying Special Nuclear Materials," INFORMS Journal on Computing, INFORMS, vol. 26(1), pages 45-58, February.
    3. Roberto Asín Achá & Dorit S. Hochbaum & Quico Spaen, 2020. "HNCcorr: combinatorial optimization for neuron identification," Annals of Operations Research, Springer, vol. 289(1), pages 5-32, June.
    4. Ruriko Yoshida & Kenji Fukumizu & Chrysafis Vogiatzis, 2019. "Multilocus phylogenetic analysis with gene tree clustering," Annals of Operations Research, Springer, vol. 276(1), pages 293-313, May.
    5. Dorit S. Hochbaum, 2013. "A Polynomial Time Algorithm for Rayleigh Ratio on Discrete Variables: Replacing Spectral Techniques for Expander Ratio, Normalized Cut, and Cheeger Constant," Operations Research, INFORMS, vol. 61(1), pages 184-198, February.

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