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Unsupervised Image Segmentation Based on Local pixel Clustering and Low-Level Region Merging

Abstract : Heterogeneous image segmentation is one of the most important tasks in image processing. It consists in partitioning the image into a set of disjoint regions. In this paper, we propose a new unsupervised image segmentation method that we call Unsupervised Image Segmentation (UIS). Our proposal performs an efficient image partition efficiently into primitive regions. This process is ensured by a local adaptive Kmeans and a novel centroids initialization. Then, similar sets are agglomerated to form homogeneous regions. For that, a low-level feature merging is employed according to a hierarchical linkage approach. Finally, in case of over-segmentation, appearing outlier regions are removed using a post process stage. Therefore, the UIS method allows to determine automatically the image region number. Indeed, it extends the Kmeans clustering to obtain meaningful regions. Several experiments were conducted using two heterogeneous image datasets. A comparison with well-known segmentation methods was also performed using the Liu's factor measure.
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https://hal-upec-upem.archives-ouvertes.fr/hal-01309999
Contributor : Rostom Kachouri <>
Submitted on : Sunday, May 1, 2016 - 2:30:00 PM
Last modification on : Wednesday, February 26, 2020 - 7:06:07 PM
Long-term archiving on: : Tuesday, May 24, 2016 - 4:07:54 PM

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  • HAL Id : hal-01309999, version 1

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Rostom Kachouri, Mahmoud Soua, Mohamed Akil. Unsupervised Image Segmentation Based on Local pixel Clustering and Low-Level Region Merging. 2nd IEEE International Conference on Advanced Technologies for Signal and Image Processing ATSIP'16, Mar 2016, Monastir, Tunisia. ⟨hal-01309999⟩

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