Patch Growing: Object segmentation using spatial coherence of local patches

  • Authors:

  • Marc Masias;Albert Torrent;Xavier Lladó;Jordi Freixenet

  • Affiliations:

  • Institute of Informatics and Applications, University of Girona, Girona, Spain;Institute of Informatics and Applications, University of Girona, Girona, Spain;Institute of Informatics and Applications, University of Girona, Girona, Spain;Institute of Informatics and Applications, University of Girona, Girona, Spain

  • Venue:
  • Proceedings of the 2009 conference on Artificial Intelligence Research and Development: Proceedings of the 12th International Conference of the Catalan Association for Artificial Intelligence
  • Year:
  • 2009

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Abstract

Object segmentation is a challenging and important problem in computer vision. The difficulties to obtain accurate segmentations using only the traditional Top-down or Bottom-up approaches have introduced new proposals based on the idea of combining them in order to obtain better results. In this paper we present a novel approach for object segmentation based on the following two steps: 1) oversegment the image in homogeneous regions using a Region Growing algorithm (Bottom-up), and 2) use prior knowledge about the object appearence (local patches and spatial coherence) from annotated images to validate and merge the regions that belong to the object (Top-down). Our experiments using different object classes from the well-known TUD and the Weizmann databases show that we are able to obtain good object segmentations from a generalistic segmentation method.