A taxonomy for texture description and identification
A taxonomy for texture description and identification
Colour image segmentation and labeling through multiedit-condensing
Pattern Recognition Letters
Direct computation of shape cues using scale-adapted spatial derivative operators
International Journal of Computer Vision - Special issue: machine vision research at the Royal Institute of Technology
Signal Processing for Computer Vision
Signal Processing for Computer Vision
Empirical evaluation of dissimilarity measures for color and texture
Computer Vision and Image Understanding - Special issue on empirical evaluation of computer vision algorithms
Digital Image Processing
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Color Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Framework for Low Level Feature Extraction
ECCV '94 Proceedings of the Third European Conference-Volume II on Computer Vision - Volume II
Detecting, localizing and grouping repeated scene elements from an image
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
VMV '01 Proceedings of the Vision Modeling and Visualization Conference 2001
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Knowledge and Data Engineering
Estimation of Structured Gaussian Mixtures: The Inverse EM Algorithm
IEEE Transactions on Signal Processing - Part I
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Segmentation of color images using multiscale clustering and graph theoretic region synthesis
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A hierarchical approach to color image segmentation using homogeneity
IEEE Transactions on Image Processing
Unsupervised multiscale color image segmentation based on MDL principle
IEEE Transactions on Image Processing
Adaptive color segmentation-a comparison of neural and statistical methods
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
LS-SVM based image segmentation using color and texture information
Journal of Visual Communication and Image Representation
Evaluating a color-based active basis model for object recognition
Computer Vision and Image Understanding
Pattern Recognition Letters
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This paper presents work on accurate image segmentation utilizing local image characteristics. Image features are measured by employing an appropriate Gabor filter with adaptively chosen size, orientation, frequency and phase for each pixel. An image property called phase divergence is used for the selection of the appropriate filter size. Characteristic features related to the change in brightness, color, texture and position are extracted for each pixel at the selected size of the filter. In order to cluster the pixels into different regions, the joint distribution of these pixel features is modeled by a mixture of Gaussians utilizing three variants of the expectation maximization (EM) algorithm. The three different versions of EM used in this work for unsupervised clustering are: (1) penalized EM, (2) penalized stochastic EM, and (3) penalized inverse EM. Given the desired number of Gaussian mixture components, all three EM algorithms estimate the parameters of the mixture of Gaussians model that represents the joint distribution of pixel features. We determine the value of the number of models that best suits the natural number of clusters present in the image based on the Schwarz criterion, which maximizes the posterior probability of the number of groups given the samples of observation. This segmentation algorithm has been tested on the images of the Berkeley segmentation benchmark and the performance has demonstrated the effectiveness, accuracy and superiority of the proposed method.