Machine Learning
Fast Approximate Energy Minimization via Graph Cuts
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
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scenery image segmentation using support vector machines
Fundamenta Informaticae
Toward Objective Evaluation of Image Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
A customized Gabor filter for unsupervised color image segmentation
Image and Vision Computing
Prediction of aeration efficiency on stepped cascades by using least square support vector machines
Expert Systems with Applications: An International Journal
Colour image segmentation using homogeneity method and data fusion techniques
EURASIP Journal on Advances in Signal Processing - Image processing and analysis in biomechanics
Benchmarking Image Segmentation Algorithms
International Journal of Computer Vision
Interactive image segmentation by maximal similarity based region merging
Pattern Recognition
Accurate segmentation of dermoscopic images by image thresholding based on type-2 fuzzy logic
IEEE Transactions on Fuzzy Systems
An edge-weighted centroidal Voronoi tessellation model for image segmentation
IEEE Transactions on Image Processing
Automatic image segmentation by dynamic region growth and multiresolution merging
IEEE Transactions on Image Processing
Generalizing edge detection to contour detection for image segmentation
Computer Vision and Image Understanding
A fast and robust image segmentation using FCM with spatial information
Digital Signal Processing
Multivariate image segmentation using semantic region growing with adaptive edge penalty
IEEE Transactions on Image Processing
A modified support vector machine and its application to image segmentation
Image and Vision Computing
Fingerprint Image Segmentation Based on Support Vector Machine
ICOIP '10 Proceedings of the 2010 International Conference on Optoelectronics and Image Processing - Volume 01
Color image segmentation using pixel wise support vector machine classification
Pattern Recognition
A graph-based framework for sub-pixel image segmentation
Theoretical Computer Science
Colour image segmentation using fuzzy clustering techniques and competitive neural network
Applied Soft Computing
Computer Vision and Image Understanding
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Image segmentation with a hybrid ensemble of one-class support vector machines
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
Medical Image Categorization and Retrieval for PACS Using the GMM-KL Framework
IEEE Transactions on Information Technology in Biomedicine
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Multiregion Image Segmentation by Parametric Kernel Graph Cuts
IEEE Transactions on Image Processing
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Image segmentation partitions an image into nonoverlapping regions, which ideally should be meaningful for a certain purpose. Automatic segmentation of images is a very challenging fundamental task in computer vision and one of the most crucial steps toward image understanding. In recent years, many image segmentation algorithms have been developed, but they are often very complex and some undesired results occur frequently. In this paper, we present an effective color image segmentation approach based on pixel classification with least squares support vector machine (LS-SVM). Firstly, the pixel-level color feature, Homogeneity, is extracted in consideration of local human visual sensitivity for color pattern variation in HSV color space. Secondly, the image pixel's texture features, Maximum local energy, Maximum gradient, and Maximum second moment matrix, are represented via Gabor filter. Then, both the pixel-level color feature and texture feature are used as input of LS-SVM model (classifier), and the LS-SVM model (classifier) is trained by selecting the training samples with Arimoto entropy thresholding. Finally, the color image is segmented with the trained LS-SVM model (classifier). This image segmentation not only can fully take advantage of the local information of color image, but also the ability of LS-SVM classifier. Experimental evidence shows that the proposed method has very effective segmentation results and computational behavior, and decreases the time and increases the quality of color image segmentation in comparison with the state-of-the-art segmentation methods recently proposed in the literature.