Fast label: easy and efficient solution of joint multi-label and estimation problems

G. Sundaramoorthi, B.W. Hong
IEEE Conference on Computer Vision and Pattern Recognition, (2014)

Fast label: easy and efficient solution of joint multi-label and estimation problems

Keywords

Joint multi-label, Estimation problem

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.

Code

DOI: 10.1109/CVPR.2014.400

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