Scenery image segmentation using support vector machines

Authors:
Yuan-Hui Yu;Chin-Chen Chang
Affiliations:
Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi, Taiwan, 621, R.O.C.;Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi, Taiwan, 621, R.O.C.
Venue:
Fundamenta Informaticae
Year:
2003

Citing 7
Cited 5

The nature of statistical learning theory

The nature of statistical learning theory
An introduction to support Vector Machines: and other kernel-based learning methods

An introduction to support Vector Machines: and other kernel-based learning methods
A Tutorial on Support Vector Machines for Pattern Recognition

Data Mining and Knowledge Discovery
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying

IEEE Transactions on Pattern Analysis and Machine Intelligence
Perfect image segmentation using pulse coupled neural networks

IEEE Transactions on Neural Networks
An introduction to kernel-based learning algorithms

IEEE Transactions on Neural Networks
A comparison of methods for multiclass support vector machines

IEEE Transactions on Neural Networks

Color image segmentation using pixel wise support vector machine classification

Pattern Recognition
Color image segmentation using automatic pixel classification with support vector machine

Neurocomputing
A pixel-based color image segmentation using support vector machine and fuzzy C-means

Neural Networks
LS-SVM based image segmentation using color and texture information

Journal of Visual Communication and Image Representation
Color texture segmentation based on image pixel classification

Engineering Applications of Artificial Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents an effective and efficient method for solving scenery image segmentation by applying the SVMs methodology. Scenery image segmentation is regarded as a data classification problem, and is effectively answered by the proposed method in this paper. Using the model selection in our system architecture, our system is relatively simple compared to other conventional heuristic image segmentation approaches yet demonstrates promising classification results.