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Multi-view clustering aims to achieve more promising clustering results than single-view clustering by exploring the multi-view information. Since statistic properties of different views are diverse, even incompatible, few approaches implement multi-view clustering based on the concatenated features directly. However, feature concatenation is a natural way to combine multiple views. To this end, this paper proposes a novel multi-view subspace clustering approach dubbed Feature Concatenation Multi-view Subspace Clustering (FCMSC). Specifically, by exploring the consensus information, multi-view data are concatenated into a joint representation firstly, then, l2,1-norm is integrated into the objective function to deal with the sample-specific and cluster-specific corruptions of multiple views for benefiting the clustering performance. Furthermore, by introducing graph Laplacians of multiple views, a graph regularized FCMSC is also introduced to explore both the consensus information and complementary information for clustering. It is noteworthy that the obtained coefficient matrix is not derived by directly applying the Low-Rank Representation (LRR) to the joint view representation simply. Finally, an effective algorithm based on the Augmented Lagrangian Multiplier (ALM) is designed to optimized the objective functions. Comprehensive experiments on six real world datasets illustrate the superiority of the proposed methods over several state-of-the-art approaches for multi-view clustering.

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To address the surface reconstruction issue, this paper proposes an efficient and accurate approach for registration of multiview range scans. It has a good objective function designed, where all multiview registration parameters are involved. To solve this function, the coarse-to-fine approach is proposed, where each range scan should be sequentially registered to a coarse surface model, which is reconstructed by other scans with initial multiview alignment. By applying the trimmed iterative closest point algorithm, it can sequentially obtain good multiview registration results for each scan, which can then be immediately utilized to refine the coarse surface model for registration of other scans. To acquire accurate surface model, several rounds of update should be applied to all range scans involved in the multiview registration. With the increase of update round, it can finally obtain the accurate surface model. Experimental results on public data sets illustrate its superiority over previous approaches. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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