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Communication Dans Un Congrès Année : 2017

A Convex Approach to K-means Clustering and Image Segmentation

Résumé

A new convex formulation of data clustering and image segmentation is proposed, with fixed number K of regions and possible penalization of the region perimeters. So, this problem is a spatially regularized version of the K-means problem, a.k.a. piecewise constant Mumford–Shah problem. The proposed approach relies on a discretization of the search space; that is, a finite number of candidates must be specified, from which the K centroids are determined. After reformulation as an assignment problem, a convex relaxation is proposed, which involves a kind of l 1,infinity norm ball. A splitting of it is proposed, so as to avoid the costly projection onto this set. Some examples illustrate the efficiency of the approach.
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Dates et versions

hal-01504799 , version 1 (10-04-2017)
hal-01504799 , version 2 (10-07-2017)
hal-01504799 , version 3 (05-12-2017)

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  • HAL Id : hal-01504799 , version 3

Citer

Laurent Condat. A Convex Approach to K-means Clustering and Image Segmentation. EMMCVPR 2017 - 11th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition, Oct 2017, Venise, Italy. ⟨hal-01504799v3⟩
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