中文摘要：

梯度特征对线性光照变化保持不变性，而稀疏编码方法能从图像数据点中得到数据的统计特性．多视图聚类算法是把同一聚类中的不同属性集合视为不同视图，考虑不同视图的重要性进行协同聚类．文中提出基于多视图聚类的图像边缘检测算法，将两种特征结合在一个统一的多视图聚类框架中，从而有效提高边缘检测的鲁棒性．该算法使用图像局部特征与稀疏编码结合的方式训练模型，并增加图像像素的空间信息和曲率信息的约束获得图像全局特征，保证检测边缘的准确性和区域一致性．在两个公开的数据库上的实验表明文中算法的可行性和有效性．

英文摘要：

The gradient feature gives an invariant description for linear lighting changes while sparse coding methods can exploit the data statistics from the image data point. In multi-view clustering algorithm, different attributes set in the same cluster are considered as different views, and the importance of different views is taken into account for co-clustering. An algorithm based on multi-view clustering for image contour detection is proposed and it integrates both features into a unified multi-view clustering framework to effectively improve the robustness of the detection system. The combination of image local features and sparse code features is utilized to train model, and the spatial information and curvature information of the image pixels are added to obtain the global features and ensure the accuracy of the contour detection and region consistency. Experiments on two large public available datasets show the feasibility and effectiveness of the proposed algorithm.