Parallel image segmentation using modified Hopfield model
Pattern Recognition Letters - Special issue on artificial neural networks
Unsupervised Texture Segmentation in a Deterministic Annealing Framework
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Color Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pairwise Data Clustering by Deterministic Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift Based Clustering in High Dimensions: A Texture Classification Example
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Deterministic annealing EM and its application in natural image segmentation
CIS'04 Proceedings of the First international conference on Computational and Information Science
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic watershed segmentation of randomly textured color images
IEEE Transactions on Image Processing
Computers & Mathematics with Applications
Detection and classification of areca nuts with machine vision
Computers & Mathematics with Applications
A novel color detection method based on HSL color space for robotic soccer competition
Computers & Mathematics with Applications
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In this paper, we propose an unsupervised segmentation algorithm for color images based on Gaussian mixture models (GMMs). The number of mixture components is determined automatically by adaptive mean shift, in which local clusters are estimated by repeatedly searching for higher density points in feature vector space. For the estimation of parameters of GMMs, the mean field annealing expectation-maximization (EM) is employed. The mean field annealing EM provides a global optimal solution to overcome the local maxima problem in a mixture model. By combining the adaptive mean shift and the mean field annealing EM, natural color images are segmented automatically without over-segmentation or isolated regions. The experiments show that the proposed algorithm can produce satisfactory segmentation without any a priori information.