基于训练样本自动选取的SVM彩色图像分割方法

图像分割是模式识别、图像理解、计算机视觉等领域的重要研究内容。基于支持向量机((Support Vcctor Ma- chine, SVM)的方法现已广泛应用于图像分割，但其在训练样本的选取上大多是人工选择，这降低了图像分割的自适 应性，且影响了SVM的分类性能。提出一种基于训练样本自动选取的SVM彩色图像分割方法，算法首先使用模糊 C均值(Fuzzy C-Mcans, FCM)聚类算法自动获取训练样本，然后分别提取图像颜色特征和纹理特征，将其作为SVM 模型训练样本的特征属性进行训练，最后用训练好的分类器对图像进行分割。实验结果表明，提出的方法可取得很好 的分割结果。

Color Image Segmentation SVM Approach Based on Training Samples Automatic Selection

Image segmentation is an important research field of pattern recognition, image understanding and computer vision. Support vector machine (SVM) is now widely used in image segmentation, but the training samples are usually selected artificially. hhis will reduce the self-adaptability and affect the classification performance of image segmenta- lion. This paper presented a color image segmentation SVM approach based on training samples automatic selection. First,Fuzzy C-Means (FCM) clustering algorithm was used to obtain the training samples for SVM automatically. hhen, color and texture features were extracted from the image as attributes of training samples of SVM. Finally, the images were segmented by the trained classifier. The experiment results demonstrate that the proposed approach can a- chieve good segmentation performance.