The Design and Use of Steerable Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Segmentation of multispectral remote sensing images using active support vector machines
Pattern Recognition Letters
Scenery image segmentation using support vector machines
Fundamenta Informaticae
Toward Objective Evaluation of Image Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Expert Systems with Applications: An International Journal
Journal of Visual Communication and Image Representation
A TSVM Based Semi-Supervised Approach to SAR Image Segmentation
ETTANDGRS '08 Proceedings of the 2008 International Workshop on Education Technology and Training & 2008 International Workshop on Geoscience and Remote Sensing - Volume 01
Benchmarking Image Segmentation Algorithms
International Journal of Computer Vision
Synergistic arc-weight estimation for interactive image segmentation using graphs
Computer Vision and Image Understanding
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
A fast and robust image segmentation using FCM with spatial information
Digital Signal Processing
A color- and texture-based image segmentation algorithm
Machine Graphics & Vision International Journal
Segmentation by Fusion of Histogram-Based -Means Clusters in Different Color Spaces
IEEE Transactions on Image Processing
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Automatic segmentation of images is a very challenging fundamental task in computer vision and one of the most crucial steps toward image understanding. In this paper, we present a color image segmentation using automatic pixel classification with support vector machine (SVM). First, the pixel-level color feature is extracted in consideration of human visual sensitivity for color pattern variations, and the image pixel's texture feature is represented via steerable filter. Both the pixel-level color feature and texture feature are used as input of SVM model (classifier). Then, the SVM model (classifier) is trained by using fuzzy c-means clustering (FCM) with the extracted pixel-level features. Finally, the color image is segmented with the trained SVM model (classifier). This image segmentation not only can fully take advantage of the local information of color image, but also the ability of SVM classifier. Experimental evidence shows that the proposed method has a very effective segmentation results and computational behavior, and decreases the time and increases the quality of color image segmentation in compare with the state-of-the-art segmentation methods recently proposed in the literature.