An Evolutional Learning Algorithm Based on Weighted Likelihood for Image Segmentation

• 作者: Li Peng Liu Yong-Mei Sun Guang-Lu Yu Lin-Sen
• 来源期刊: 国际计算机前沿大会会议论文集
• 学科: 社会科学
• 关键词: image SEGMENTATION MIXTURE models spatial CONSTRAINT EM algorithm
• 年，卷（期）: 2015,（1）
• 页码范围: 61-62
• 页数: 2页
• 分类号: C5

摘要

Due to the coupling of model parameters, most spatial mixture models for image segmentation can not directly computed by EM algorithm. The paper proposes an evolutional learning algorithm based on weighted likelihood of mixture models for image segmentation. The proposed algorithm consists of multiple generations of learning algorithm, and each stage of learning algorithm corresponds to an EM algorithm of spatially constraint independent mixture model. The smoothed EM result in spatial domain of each stage is considered as the supervision information to guide the next stage clustering. The spatial constraint information is thus incorporated into the independent mixture model. So the coupling problem of the spatial model parameters can be avoided at a lower computational cost. Experiments using synthetic and real images are presented to show the efficiency of the proposed algorithm.