TY - GEN
T1 - FAST LABEL: Easy and efficient solution of joint multi-label and estimation problems
AU - Sundaramoorthi, Ganesh
AU - Hong, Byungwoo
N1 - KAUST Repository Item: Exported on 2020-10-01
PY - 2014/6
Y1 - 2014/6
N2 - 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.
AB - 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.
UR - http://hdl.handle.net/10754/575822
UR - http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6909796
UR - http://www.scopus.com/inward/record.url?scp=84911408629&partnerID=8YFLogxK
U2 - 10.1109/CVPR.2014.400
DO - 10.1109/CVPR.2014.400
M3 - Conference contribution
SN - 9781479951178; 9781479951178
SP - 3126
EP - 3133
BT - 2014 IEEE Conference on Computer Vision and Pattern Recognition
PB - Institute of Electrical and Electronics Engineers (IEEE)
ER -