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Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2023,2,28]]},"abstract":"<jats:p>\n            In this paper, we propose an efficient point cloud classification method via manifold learning based feature representation. Different from conventional methods, we use manifold learning algorithms to embed point cloud features for better considering the geometric continuity on the surface. Then, the nature of point cloud can be acquired in low dimensional space, and after being concatenated with features in the original three-dimensional (3D) space, both the capability of feature representation and the classification network performance can be improved. We explore three traditional manifold algorithms (i.e., Isomap, Locally-Linear Embedding, and Laplacian eigenmaps) in detail, and finally, we select the\n            <jats:bold>Locally-Linear Embedding (LLE)<\/jats:bold>\n            algorithm due to its low complexity and locality consistency preservation. Furthermore, we propose a\n            <jats:bold>neural network based manifold learning (NNML)<\/jats:bold>\n            method to implement manifold learning based non-linear projection. Experiments demonstrate that the proposed two manifold learning methods can obtain better performances than the state-of-the-art methods, and the obtained\n            <jats:bold>mean class accuracy (mA)<\/jats:bold>\n            and\n            <jats:bold>overall accuracy (oA)<\/jats:bold>\n            can reach 91.4% and 94.4%, respectively. Moreover, because of the improved feature learning capability, the proposed NNML method can also have better classification accuracy on models with prominent geometric shapes. To further demonstrate the advantages of PointManifold, we extend it as a plug and play method for point cloud classification task, which can be directly used with existing methods and gain a significant improvement.\n          <\/jats:p>\n          <jats:p\/>","DOI":"10.1145\/3539611","type":"journal-article","created":{"date-parts":[[2022,7,29]],"date-time":"2022-07-29T11:49:08Z","timestamp":1659095348000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":23,"title":["Exploiting Manifold Feature Representation for Efficient Classification of 3D Point Clouds"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8912-2968","authenticated-orcid":false,"given":"Dinghao","family":"Yang","sequence":"first","affiliation":[{"name":"School of Electronic and Computer Engineering, Peking University, and also Peng Cheng Laboratory, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7429-5495","authenticated-orcid":false,"given":"Wei","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Electronic and Computer Engineering, Peking University, and also Peng Cheng Laboratory, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0140-0949","authenticated-orcid":false,"given":"Ge","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electronic and Computer Engineering, Peking University, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5212-3393","authenticated-orcid":false,"given":"Hui","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Control Science and Engineering, Shandong University, Shangdong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3431-2021","authenticated-orcid":false,"given":"Junhui","family":"Hou","sequence":"additional","affiliation":[{"name":"Department of Computer Science, City University of Hong Kong, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7484-7261","authenticated-orcid":false,"given":"Sam","family":"Kwong","sequence":"additional","affiliation":[{"name":"Department of Computer Science, City University of Hong Kong, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,1,23]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"Accessed in Feb. 2022. 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Point2Node: Correlation learning of dynamic-node for point cloud feature modeling. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 10925\u201310932."},{"key":"e_1_3_2_7_2","article-title":"Learning semantic segmentation of large-scale point clouds with random sampling","author":"Hu Qingyong","year":"2021","unstructured":"Qingyong Hu, Bo Yang, Linhai Xie, Stefano Rosa, Yulan Guo, Zhihua Wang, Niki Trigoni, and Andrew Markham. 2021. Learning semantic segmentation of large-scale point clouds with random sampling. 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