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Finding semantic structures in image hierarchies using Laplacian graph energy


Reference:

Song, Y.-Z., Arbelaez, P., Hall, P., Li, C. and Balikai, A., 2010. Finding semantic structures in image hierarchies using Laplacian graph energy. In: Daniilidis, K., Maragos, P. and Paragios, N., eds. Computer Vision, ECCV 2010: 11th European Conference on Computer Vision, Heraklion, Crete, Greece, September 5-11, 2010, Proceedings, Part IV. Berlin: Springer, pp. 694-707. (Lecture Notes in Computer Science; 6314)

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    Official URL:

    http://dx.doi.org/10.1007/978-3-642-15561-1_50

    Abstract

    Many segmentation algorithms describe images in terms of a hierarchy of regions. Although such hierarchies can produce state of the art segmentations and have many applications, they often contain more data than is required for an efficient description. This paper shows Laplacian graph energy is a generic measure that can be used to identify semantic structures within hierarchies, independently of the algorithm that produces them. Quantitative experimental validation using hierarchies from two state of art algorithms show we can reduce the number of levels and regions in a hierarchy by an order of magnitude with little or no loss in performance when compared against human produced ground truth. We provide a tracking application that illustrates the value of reduced hierarchies.

    Details

    Item Type Book Sections
    CreatorsSong, Y.-Z., Arbelaez, P., Hall, P., Li, C. and Balikai, A.
    EditorsDaniilidis, K., Maragos, P. and Paragios, N.
    DOI10.1007/978-3-642-15561-1_50
    DepartmentsFaculty of Science > Computer Science
    Research CentresMedia Technology Research Centre
    Publisher StatementSong_LNCS_2010.pdf: The original publication is available at www.springerlink.com
    StatusPublished
    ID Code21842
    Additional Information11th European Conference on Computer Vision, ECCV 2010. 5-11 September 2010. Heraklion, Crete, Greece.

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