关键词: 彩色图像分割, 区域生长算法, 支持向量机, 形态学处理, 图像增强, 标记背景, 特征提取, 颜色空间

Abstract:

The noise and gray scale growth in the traditional area may cause voids and over-segmentation, which will seriously affect the image segmentation effect. To solve this problem, this study proposes a color label image segmentation algorithm combining SVM and region growth. The algorithm uses the color information and texture of the label part and product part as positive and negative samples of the SVM, which effectively improves the efficiency and the accuracy of image segmentation. When extracting positive and negative samples, the improved region growth algorithm is used to segment feature areas and extract features. Theoretical analysis and experimental results show that the proposed algorithm has a mechanism for correcting error detection information, improves the segmentation efficiency, prevents the over-segmentation phenomenon of traditional regional growth algorithms during segmentation, and has good robustness, which lays the foundation for later expansion.

Key words: color image segmentation, region growth algorithm, support vector machine, morphological processing, image enhancement, labeling background, feature extraction, color space