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IEEE Trans Neural Netw. 2011 Mar;22(3):447-60. doi: 10.1109/TNN.2010.2101614. Epub 2011 Jan 20.

MDS-based multiresolution nonlinear dimensionality reduction model for color image segmentation.

Author information

1
Département d’Informatique et de Recherche Opérationnelle, Faculté des Arts et des Sciences, Université de Montréal, Montréal H3C 3J7, QC, Canada. mignotte@iro.umontreal.ca

Abstract

In this paper, we present an efficient coarse-to-fine multiresolution framework for multidimensional scaling and demonstrate its performance on a large-scale nonlinear dimensionality reduction and embedding problem in a texture feature extraction step for the unsupervised image segmentation problem. We demonstrate both the efficiency of our multiresolution algorithm and its real interest to learn a nonlinear low-dimensional representation of the texture feature set of an image which can then subsequently be exploited in a simple clustering-based segmentation algorithm. The resulting segmentation procedure has been successfully applied on the Berkeley image database, demonstrating its efficiency compared to the best existing state-of-the-art segmentation methods recently proposed in the literature.

PMID:
21257375
DOI:
10.1109/TNN.2010.2101614
[Indexed for MEDLINE]

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