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    FAST LABEL: Easy and efficient solution of joint multi-label and estimation problems

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    Type
    Conference Paper
    Authors
    Sundaramoorthi, Ganesh cc
    Hong, Byungwoo
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Electrical Engineering Program
    Visual Computing Center (VCC)
    Date
    2014-06
    Permanent link to this record
    http://hdl.handle.net/10754/575822
    
    Metadata
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    Abstract
    We derive an easy-to-implement and efficient algorithm for solving multi-label image partitioning problems in the form of the problem addressed by Region Competition. These problems jointly determine a parameter for each of the regions in the partition. Given an estimate of the parameters, a fast approximate solution to the multi-label sub-problem is derived by a global update that uses smoothing and thresholding. The method is empirically validated to be robust to fine details of the image that plague local solutions. Further, in comparison to global methods for the multi-label problem, the method is more efficient and it is easy for a non-specialist to implement. We give sample Matlab code for the multi-label Chan-Vese problem in this paper! Experimental comparison to the state-of-the-art in multi-label solutions to Region Competition shows that our method achieves equal or better accuracy, with the main advantage being speed and ease of implementation.
    Citation
    Sundaramoorthi, G., & Hong, B.-W. (2014). FAST LABEL: Easy and Efficient Solution of Joint Multi-label and Estimation Problems. 2014 IEEE Conference on Computer Vision and Pattern Recognition. doi:10.1109/cvpr.2014.400
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    2014 IEEE Conference on Computer Vision and Pattern Recognition
    Conference/Event name
    27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014
    ISBN
    9781479951178; 9781479951178
    DOI
    10.1109/CVPR.2014.400
    ae974a485f413a2113503eed53cd6c53
    10.1109/CVPR.2014.400
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