Boundary Detection Method Based on Supervising for Small Sample Size Problem

摘要：

In this paper, we address segmentation of the image with gray and texture measurements together. Combining the filter banks and improved K-Means clustering, the texton is extracted effectively in small samples case. And then, a model used for boundary detection is proposed. This model combines multiple cues, such as gray and texture feature. Proposed model trains parameters using human labeled images and therefore the output of trained model is detected boundary. Finally, we optimize the extracted boundary. The results show that our method not only can accurately detect the boundary but also reduce the time complexity in small samples case compared to the existing method.

关键词： supervised learning small sample size problem boundary detection texton feature extractio improved K-Means clustering

作者: Liang Gao Xiaoyun Liu

作者单位: School of Automation Engineering, University of Electronic Science and Technology of China SAEUESTC Chengdu, China

会议类型: 国际会议

会议名称: 2011 4th International Congress on Image and Signal Processing(第四届图像与信号处理国际学术会议 CISP 2011)

会议地点: 上海

会议语种:英文

页码: 1232-1236

在线出版日期: 2011-10-15（万方平台首次上网日期，不代表论文的发表时间）