MDS-Based Multiresolution Nonlinear Dimensionality Reduction Model for Color Image Segmentation
TL;DR: An efficient coarse-to-fine multiresolution framework for multidimensional scaling and its performance on a large-scale nonlinear dimensionality reduction and embedding problem in a texture feature extraction step for the unsupervised image segmentation problem is demonstrated.
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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.
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Citations
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