Inducing Semantic Segmentation from an Example View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2006

AUTHORS

Yaar Schnitman , Yaron Caspi , Daniel Cohen-Or , Dani Lischinski

ABSTRACT

Segmenting an image into semantically meaningful parts is a fundamental and challenging task in computer vision. Automatic methods are able to segment an image into coherent regions, but such regions generally do not correspond to complete meaningful parts. In this paper, we show that even a single training example can greatly facilitate the induction of a semantically meaningful segmentation on novel images within the same domain: images depicting the same, or similar, objects in a similar setting. Our approach constructs a non-parametric representation of the example segmentation by selecting patch-based representatives. This allows us to represent complex semantic regions containing a large variety of colors and textures. Given an input image, we first partition it into small homogeneous fragments, and the possible labelings of each fragment are assessed using a robust voting procedure. Graph-cuts optimization is then used to label each fragment in a globally optimal manner. More... »

PAGES

373-384

References to SciGraph publications

  • 2002. Class-Specific, Top-Down Segmentation in COMPUTER VISION — ECCV 2002
  • Book

    TITLE

    Computer Vision – ACCV 2006

    ISBN

    978-3-540-31244-4
    978-3-540-32432-4

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/11612704_38

    DOI

    http://dx.doi.org/10.1007/11612704_38

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1035584643


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