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MDS-Based Multiresolution Nonlinear Dimensionality Reduction Model for Color Image Segmentation

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1 Author(s)
Mignotte, M. ; Fac. des Arts et des Sci., Univ. de Montreal, Montreal, QC, Canada

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.

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Neural Networks, IEEE Transactions on  (Volume:22 ,  Issue: 3 )