A mutual GrabCut method to solve co-segmentation View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2013-12

AUTHORS

Zhisheng Gao, Peng Shi, Hamid Reza Karimi, Zheng Pei

ABSTRACT

Co-segmentation aims at segmenting common objects from a group of images. Markov random field (MRF) has been widely used to solve co-segmentation, which introduces a global constraint to make the foreground similar to each other. However, it is difficult to minimize the new model. In this paper, we propose a new Markov random field-based co-segmentation model to solve co-segmentation problem without minimization problem. In our model, foreground similarity constraint is added into the unary term of MRF model rather than the global term, which can be minimized by graph cut method. In the model, a new energy function is designed by considering both the foreground similarity and the background consistency. Then, a mutual optimization approach is used to minimize the energy function. We test the proposed method on many pairs of images. The experimental results demonstrate the effectiveness of the proposed method. More... »

PAGES

20

References to SciGraph publications

  • 2010. Cosegmentation Revisited: Models and Optimization in COMPUTER VISION – ECCV 2010
  • 2008-09. Shape-Based Mutual Segmentation in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/1687-5281-2013-20

    DOI

    http://dx.doi.org/10.1186/1687-5281-2013-20

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1002713026


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