A new sparse representation-based object segmentation framework

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• 作者：Jincao Yao ; Huimin Yu ; Roland Hu
• 关键词：Shape segmentation ; Sparse representation ; Independent component ; Pose alignment
• 刊名：The Visual Computer
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：33
• 期：2
• 页码：179-192
• 全文大小：
• 刊物类别：Computer Science
• 刊物主题：Computer Graphics; Computer Science, general; Artificial Intelligence (incl. Robotics); Image Processing and Computer Vision;
• 出版者：Springer Berlin Heidelberg
• ISSN：1432-2315
• 卷排序：33

文摘

In this paper, a novel sparse representation-based object segmentation model is proposed. The model follows from a new energy function that combines the level-set-based sparse representation and the independent component-based shape representation within a unified framework. Before the optimization of the proposed energy, a set of training shapes is firstly projected into the shape space spanned by the independent components. For an arbitrary input shape similar to some of the elements in the training set, the minimization of the energy will automatically recover a sparse shape combination according to the neighbors in the projected shape space to guide the variational image segmentation. We test our model on both public datasets and real applications, and the experimental results show the superior segmentation capabilities of the proposed model.