MDS-based multiresolution nonlinear dimensionality reduction model for color image segmentation.
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.
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
Max
M
Mignotte
D000465:Algorithms; D001185:Artificial Intelligence; D003116:Color / Q000592:standards; D055253:Color Vision / Q000502:physiology; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008959:Models, Neurological; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D010363:Pattern Recognition, Automated / Q000379:methods*; D011337:Probability Learning; D012985:Software Design