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Conference papers

Relaxed Cheeger Cut for Image Segmentation

Abstract : Motivated by recent advances in spectral clustering that show the relation between the non linear p-Laplacian graph operator and the Cheeger cut problem, we propose to study and apply this methodology for image segmentation. Based on a ℓ 1 relaxation of the initial clustering problem, we show that these methods can outperform usual well-known graph based approaches, e.g., min-cut/max-flow algorithm or ℓ 2 spectral cluster- ing, for unsupervised and very weakly supervised image segmentation. Experimental results demonstrate the benefits and the relevance of the proposed methodology especially for noisy image or when very few pixels are labeled for interactive image segmentation.
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Submitted on : Sunday, December 23, 2012 - 6:49:19 PM
Last modification on : Saturday, June 25, 2022 - 10:33:01 AM
Long-term archiving on: : Thursday, December 15, 2016 - 3:22:41 PM


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  • HAL Id : hal-00708970, version 1


Ludovic Paulhac, Vinh-Thong Ta, Rémi Mégret. Relaxed Cheeger Cut for Image Segmentation. International Conference on Pattern Recognition, 2012, Tsukuba, Japan. ⟨hal-00708970⟩



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