IDEAS home Printed from https://ideas.repec.org/a/nat/nature/v442y2006i7104d10.1038_nature04977.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/nature04977
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1038/nature04977?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    4. 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.
    5. 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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:nature:v:442:y:2006:i:7104:d:10.1038_nature04977. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.