Abstract 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.
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2016, Vol. 29 
