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Unsupervised RGB-D image segmentation using joint clustering and region merging

Abstract : Recent advances in imaging sensors, such as Kinect, provide 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-axial clustering method followed by a statistical planar region merging method. We evaluate our method on the NYU depth database V2 (NYUD2) and compare with existing unsupervised RGB-D segmentation methods. Results show that our method is comparable with the state of the art methods. Moreover, it opens interesting perspectives for fusing color and geometry in an unsupervised manner.
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Contributor : Olivier Alata Connect in order to contact the contributor
Submitted on : Tuesday, July 8, 2014 - 11:38:23 AM
Last modification on : Saturday, June 25, 2022 - 10:54:57 AM


  • HAL Id : ujm-01020565, version 1



Abul Hasnat, Olivier Alata, Alain Trémeau. Unsupervised RGB-D image segmentation using joint clustering and region merging. British Machine Vision Conference (BMVC), 2014, Sep 2014, United Kingdom. pp.1-12. ⟨ujm-01020565⟩



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