Image Classification Based on Fuzzy Support Vector Machine

Authors:
Jianming Li;Shuguang Huang;Rongsheng He;Kunming Qian
Affiliations:
-;-;-;-
Venue:
ISCID '08 Proceedings of the 2008 International Symposium on Computational Intelligence and Design - Volume 01
Year:
2008

Citing 0
Cited 5

Regression application based on fuzzy ν-support vector machine in symmetric triangular fuzzy space

Expert Systems with Applications: An International Journal
An identification method of malignant and benign liver tumors from ultrasonography based on GLCM texture features and fuzzy SVM

Expert Systems with Applications: An International Journal
Fuzzy support vector regression machine with penalizing Gaussian noises on triangular fuzzy number space

Expert Systems with Applications: An International Journal
The forecasting model based on fuzzy novel ν-support vector machine

Expert Systems with Applications: An International Journal
Learning-based multiresolution transforms with application to image compression

Signal Processing

Quantified Score

Hi-index	0.01

Visualization

Abstract

As a basic two-class classifier, support vector machine (SVM) has been proved to perform well in image classification, which is one of the most common tasks of image processing. However, for the n-class problem in image classification, SVM treats it as n two-class problems, in this way, unclassifiable regions exist. In this paper, we introduce fuzzy support vector machine (FSVM) and define a membership function to classify images which are unclassifiable using conventional SVM. For the input vector of SVM and FSVM, we use combined image feature histogram. Being compared with the conventional SVM, FSVM shows the same result as SVM for the images in the classifiable regions, and for those in the unclassifiable regions, FSVM generates better result than SVM.