In the research and application of images, people are often only interested in the foreground or specific area of the image, so it is necessary to extract the specific area from the image, and image segmentation technology is the key to solving this problem. Aiming at the complex background and the color image with unclear target contour as the target image to be segmented, this paper first uses the texture and color of the image as the feature vector, and proposes an image segmentation algorithm based on SVM. The experimental results show that the segmentation accuracy is 91.23%. Secondly, in order to improve the accuracy of segmentation, the SVM algorithm is improved. The improved SVM algorithm is based on the grid search method to optimize the parameters C and g in the SVM. At the same time, the HIS color channel is added to the feature vector to obtain more Excellent SVM image segmentation model. Finally, the color image segmentation is verified and compared with the standard SVM algorithm. The experimental results show that the accuracy rate of the improved SVM algorithm reaches 97.263%, which improves the segmentation efficiency. It is verified that the improved model proposed in this paper can effectively segment complex color images.

在图像的研究和应用中，人们往往只对图像的前景或特定区域感兴趣，因此需要从图像中提取特定区域，而图像分割技术是解决这一问题的关键。针对背景复杂、目标轮廓不清晰的彩色图像作为待分割的目标图像，本文首先以图像的纹理和颜色作为特征向量，提出一种基于SVM的图像分割算法。实验结果表明分割准确率为91.23%。其次，为了提高分割精度，对SVM算法进行了改进。改进的 SVM 算法是基于网格搜索方法对 SVM 中的参数 C 和 g 进行优化。与此同时，在特征向量中加入HIS颜色通道，得到更优秀的SVM图像分割模型。最后，对彩色图像分割进行了验证，并与标准 SVM 算法进行了比较。实验结果表明，改进的SVM算法准确率达到97.263%，提高了分割效率。验证了本文提出的改进模型能够有效分割复杂的彩色图像。