TY - GEN
T1 - Multi-feature sparse representation classification method based on clustering
AU - Wang, Zeli
AU - Tang, Xiaoliang
AU - Sun, Weizhen
AU - Li, Chao
AU - Guo, Jielong
AU - Wei, Xian
N1 - Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/7/12
Y1 - 2019/7/12
N2 - 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.
AB - 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.
KW - 3D-SIFT
KW - LiDAR
KW - Object identification
KW - Sparse representation
UR - https://www.scopus.com/pages/publications/85073063671
U2 - 10.1145/3349341.3349506
DO - 10.1145/3349341.3349506
M3 - 会议稿件
AN - SCOPUS:85073063671
T3 - ACM International Conference Proceeding Series
SP - 759
EP - 763
BT - Proceedings of the 2019 International Conference on Artificial Intelligence and Computer Science, AICS 2019
PB - Association for Computing Machinery
T2 - 2019 International Conference on Artificial Intelligence and Computer Science, AICS 2019
Y2 - 12 July 2019 through 13 July 2019
ER -