基于K均值聚类和粒子群优化的多核SVM图像分割

图像分割是图像理解和计算机视觉的重要内容.针对单核SVM在进行图像分割过程中不能兼顾分割精度高和泛化性能好的问题,提出一种基于K均值聚类和优化多核SVM的图像分割算法.该算法首先运用K均值聚类算法自动选取训练样本,然后提取其颜色特征和纹理特征作为训练样本的特征属性,并使用其对构造的多核SVM分割模型进行训练,最后用粒子群优化算法对多核核参数、惩罚因子以及核权重系数联合寻优,使生成的多核SVM具有更好的分割性能.实验结果表明,本文方法在有效提取图像目标细节的同时,获得了更高的分割精度,与基于单核的SVM分割模型相比,具有更强的泛化能力.

Method for Image Segmentation Based on Optimized Multi-Kernel SVM and K-means Clustering

Image segmentation is an important topic in image understanding and computer vision. When support vector machine(SVM) is used for image segmentation, the design of its kernel and selection of the parameters directly affect the segmentation effect. Considering the problem that SVM based on single kernel could not keep the balance between the segmentation accuracy and generalization performance, an image segmentation algorithm using optimized multi-kernel SVM(OMKSVM) based on K-means clustering was proposed in this paper. According to the multi-kernel learning theory, the new multi-kernel is constructed by a linear combination of single kernels. Firstly, the K-means clustering algorithm was applied to obtain the training samples for MKSVM automatically. Then color and texture features were extracted from the image as attributes of training samples of MKSVM, Particle Swarm Optimization(PSO) algorithm was employed to optimize the kernel parameters, the weight coefficient and the punishment coefficient of SVM simultaneously. Finally the OMKSVM was obtained to segment image. Three groups of complex color image were selected to verify the correctness of the proposed method. The results demonstrate that our method can segment the color images effectively, and has stronger generalization ability comparing with the single kernel SVM-based method.