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

Ganesh Sundaramoorthi, Byungwoo Hong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

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.
Original languageEnglish (US)
Title of host publication2014 IEEE Conference on Computer Vision and Pattern Recognition
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages3126-3133
Number of pages8
ISBN (Print)9781479951178; 9781479951178
DOIs
StatePublished - Jun 2014

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