Multi-Feature Sparse Representation Classification Method Based on Clustering

In complex environments, the point cloud data obtained by LiDAR Often have shadows and occlusion, which greatly reduces the accuracy and the robustness of target classification. To solve this problem, this paper proposes a robust LiDAR point cloud recognition method, called Multi-Feature Sparse Representation Classification based on Clustering (MFSRCC). Firstly, all training data are used to generate a 3D-SIFT multi-feature dictionary. Secondly, the data are reconstructed on the basis of a complete dictionary. Finally, the sparse coefficients are clustered by K-means, and hence the classifier is constructed according to the principle of minimum cluster center value. The experimental results performed on Large-Scale Point Cloud Classification benchmark show that the proposed method can significantly improve the recognition rate of LiDAR point cloud objects, and it has strong robustness to interference information.

[1]  Xuan Tang,et al.  Reconstructible Nonlinear Dimensionality Reduction via Joint Dictionary Learning , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[2]  Milan Sonka,et al.  Registration of 3D spectral OCT volumes using 3D SIFT feature point matching , 2009, Medical Imaging.

[3]  Andreas Birk,et al.  3D forward sensor modeling and application to occupancy grid based sensor fusion , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[4]  Rachida Amjoun,et al.  Conditional Random Field for 3D Point Clouds with Adaptive Data Reduction , 2007, CW 2007.

[5]  Mubarak Shah,et al.  A 3-dimensional sift descriptor and its application to action recognition , 2007, ACM Multimedia.

[6]  Allen Y. Yang,et al.  Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  David G. Stork,et al.  Pattern Classification , 1973 .

[8]  Marc Pollefeys,et al.  Semantic3D.net: A new Large-scale Point Cloud Classification Benchmark , 2017, ArXiv.

[9]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[10]  Hao Shen,et al.  Trace Quotient Meets Sparsity: A Method for Learning Low Dimensional Image Representations , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  Chaomin Luo,et al.  Generic object recognition based on the fusion of 2D and 3D SIFT descriptors , 2015, 2015 18th International Conference on Information Fusion (Fusion).

[12]  David Suter,et al.  Conditional Random Field for 3D Point Clouds with Adaptive Data Reduction , 2007, 2007 International Conference on Cyberworlds (CW'07).

[13]  René Vidal,et al.  Sparse Subspace Clustering: Algorithm, Theory, and Applications , 2012, IEEE transactions on pattern analysis and machine intelligence.

[14]  Chun Liu,et al.  Tensor-Based Sparse Representation Classification for Urban Airborne LiDAR Points , 2017, Remote. Sens..

[15]  Michael Elad,et al.  Dictionaries for Sparse Representation Modeling , 2010, Proceedings of the IEEE.

[16]  Hongyu Li,et al.  A novel 3D ear identification approach based on sparse representation , 2013, 2013 IEEE International Conference on Image Processing.

[17]  Wei Zhang,et al.  Encrypted SVM for Outsourced Data Mining , 2015, 2015 IEEE 8th International Conference on Cloud Computing.