FAST LABEL: Easy and efficient solution of joint multi-label and estimation problems
Type
Conference PaperAuthors
Sundaramoorthi, Ganesh
Hong, Byungwoo
KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionElectrical Engineering Program
Visual Computing Center (VCC)
Date
2014-06Permanent link to this record
http://hdl.handle.net/10754/575822
Metadata
Show full item recordAbstract
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.2014.400Conference/Event name
27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014ISBN
9781479951178; 9781479951178ae974a485f413a2113503eed53cd6c53
10.1109/CVPR.2014.400