关键词: 小样本问题, 边界检测, 纹理基元, 监督学习, 分类器

Abstract: For natural images of complex texture, a supervised boundary detection method using the multi-information fusion was proposed. The texture information was introduced by quickly generating texton feature in the case of small sample. Intensity and texture gradients were further computed according to the differences of intensity and texton distributions within a pixel's neighborhood. In this way, a two-dimensional gradient feature vector was constructed, and a supervised classifier was used to adaptively detect original edge pixels. Finally, a boundary localization function was designed to determine the final edge pixels. The experimental results have demonstrated that the proposed method is faster and more effective.

Key words: small sample problem, boundary detection, texton, supervised learning, classifier