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dc.contributor.authorHell, Benjaminen_US
dc.contributor.authorMagnor, Marcusen_US
dc.contributor.editorDavid Bommes and Tobias Ritschel and Thomas Schultzen_US
dc.date.accessioned2015-10-07T05:13:36Z
dc.date.available2015-10-07T05:13:36Z
dc.date.issued2015en_US
dc.identifier.isbn978-3-905674-95-8en_US
dc.identifier.urihttp://dx.doi.org/10.2312/vmv.20151262en_US
dc.description.abstractIn this paper we present a novel way of combining the process of k-means clustering with image segmentation by introducing a convex regularizer for segmentation-based optimization problems. Instead of separating the clustering process from the core image segmentation algorithm, this regularizer allows the direct incorporation of clustering information in many segmentation algorithms. Besides introducing the model of the regularizer, we present a numerical algorithm to efficiently solve the occurring optimization problem while maintaining complete compatibility with any other gradient descent based optimization method. As a side-product, this algorithm also introduces a new way to solve the rather elaborate relaxed k-means clustering problem, which has been established as a convex alternative to the non-convex k-means problem.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectI.4.6 [IMAGE PROCESSING AND COMPUTER VISION]en_US
dc.subjectSegmentationen_US
dc.subjectRelaxationen_US
dc.titleA Convex Clustering-based Regularizer for Image Segmentationen_US
dc.description.seriesinformationVision, Modeling & Visualizationen_US
dc.description.sectionheadersImages and Videoen_US
dc.identifier.doi10.2312/vmv.20151262en_US
dc.identifier.pages87-94en_US


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  • VMV15
    ISBN 978-3-905674-95-8

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