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Graph-Based Regularization of Binary Classifiers for Texture Segmentation

Abstract : In this paper, we propose to improve a recent texture-based graph regularization model used to perform image segmentation by including a binary classifier in the process. Built upon two non-local image processing techniques, the addition of a classifier brings to our model the ability to weight texture features according to their relevance. The graph regularization process is then applied on the initial segmentation provided by the classifier in order to clear it from most imperfections. Results are presented on artificial and medical images, and compared to an active contour driven by classifiers segmentation algorithm, highlighting the increased generality and accuracy of our model.
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Contributor : Denis Maurel Connect in order to contact the contributor
Submitted on : Tuesday, July 22, 2014 - 9:37:52 AM
Last modification on : Thursday, March 3, 2022 - 5:32:01 PM


  • HAL Id : hal-01027467, version 1


Cyrille Faucheux, Julien Olivier, Romuald Boné. Graph-Based Regularization of Binary Classifiers for Texture Segmentation. 15th International Conference on Computer Analysis of Images and Patterns, Aug 2013, York, United Kingdom. pp.310-318. ⟨hal-01027467⟩



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