Joint Color-Spatial-Directional clustering and Region Merging (JCSD-RM) for unsupervised RGB-D image segmentation - Université Jean-Monnet-Saint-Étienne Access content directly
Journal Articles IEEE Transactions on Pattern Analysis and Machine Intelligence Year : 2016

Joint Color-Spatial-Directional clustering and Region Merging (JCSD-RM) for unsupervised RGB-D image segmentation

Olivier Alata

Abstract

Recent advances in depth imaging sensors provide easy access to the synchronized depth with color, called RGB-D image. In this paper, we propose an unsupervised method for indoor RGB-D image segmentation and analysis. We consider a statistical image generation model based on the color and geometry of the scene. Our method consists of a joint color-spatial-directional clustering method followed by a statistical planar region merging method. We evaluate our method on the NYU depth database and compare it with existing unsupervised RGB-D segmentation methods. Results show that, it is comparable with the state of the art methods and it needs less computation time. Moreover, it opens interesting perspectives to fuse color and geometry in an unsupervised manner.
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Dates and versions

ujm-01273670 , version 1 (12-02-2016)

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Abul Hasnat, Olivier Alata, Alain Treméau. Joint Color-Spatial-Directional clustering and Region Merging (JCSD-RM) for unsupervised RGB-D image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, ⟨10.1109/TPAMI.2015.2513407⟩. ⟨ujm-01273670⟩
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