A SVM image segmentation algorithm based on improved simulated annealing particle swarm optimization

作者：Cao Bin; Shen Xuanjing^*; Qian Qingji

来源：Journal of Computational Information Systems, 2011, 7(10): 3676-3682.

• Classification methods
• Complex background
• Foreground images
• High-dimensional
• Image segmentation algorithm
• Intensity inhomogeneity
• Machine-learning
• Non-Linearity
• Particle swarm
• Segmentation methods
• Segmentation results
• Small samples
• Support vector
• Traditional learning

Extracting the user interested foreground from the image with intensity inhomogeneity and complex backgrounds is an important issue in image segmentation. The features of the complex background image and the user interested foreground image can be extracted respectively. Then we can use the machine learning theory to segment the image. The traditional learning classification methods include artificial neural networks and so on. But under the conditions of small sample, high-dimensional nonlinearity, the effects of these methods are not very good. However, the support vector machine under small samples, high-dimensional nonlinearity conditions achieves good results. So the support vector machine optimized by improved simulated annealing particle swarm optimization is used to segment the foreground image from intensity inhomogeneity complex background image in this paper. The image segmentation method proposed in this paper can obtain good image segmentation results.

• 出版日期2011