The nature of statistical learning theory
The nature of statistical learning theory
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
Texture representation based on pattern map
Signal Processing
Scenery image segmentation using support vector machines
Fundamenta Informaticae
Toward Objective Evaluation of Image Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multilevel thresholding for image segmentation through a fast statistical recursive algorithm
Pattern Recognition Letters
Computer Vision and Image Understanding
IRGS: Image Segmentation Using Edge Penalties and Region Growing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Benchmarking Image Segmentation Algorithms
International Journal of Computer Vision
Image segmentation using information bottleneck method
IEEE Transactions on Image Processing
A fast and robust image segmentation using FCM with spatial information
Digital Signal Processing
A modified support vector machine and its application to image segmentation
Image and Vision Computing
Color image segmentation using pixel wise support vector machine classification
Pattern Recognition
Colour image segmentation using fuzzy clustering techniques and competitive neural network
Applied Soft Computing
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Kernelized structural SVM learning for supervised object segmentation
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Color Image Segmentation Based on Mean Shift and Normalized Cuts
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Fuzzy Systems
Multiregion Image Segmentation by Parametric Kernel Graph Cuts
IEEE Transactions on Image Processing
Natural Image Segmentation Based on Tree Equipartition, Bayesian Flooding and Region Merging
IEEE Transactions on Image Processing
A robust texture feature extraction using the localized angular phase
Multimedia Tools and Applications
Ensemble classification of colon biopsy images based on information rich hybrid features
Computers in Biology and Medicine
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Image segmentation partitions an image into nonoverlapping regions, which ideally should be meaningful for a certain purpose. Thus, image segmentation plays an important role in many multimedia applications. In recent years, many image segmentation algorithms have been developed, but they are often very complex and some undesired results occur frequently. By combination of Fuzzy Support Vector Machine (FSVM) and Fuzzy C-Means (FCM), a color texture segmentation based on image pixel classification is proposed in this paper. Specifically, we first extract the pixel-level color feature and texture feature of the image via the local spatial similarity measure model and localized Fourier transform, which is used as input of FSVM model (classifier). We then train the FSVM model (classifier) by using FCM with the extracted pixel-level features. Color image segmentation can be then performed through the trained FSVM model (classifier). Compared with three other segmentation algorithms, the results show that the proposed algorithm is more effective in color image segmentation.