caa a22 4500 605447802 CHVBK 20210128100133.0 cr unu---uuuuu 210128e20150801xx s 000 0 eng 10.1007/s11042-014-1866-9 doi (NATIONALLICENCE)springer-10.1007/s11042-014-1866-9 A semi-supervised incremental learning method based on adaptive probabilistic hypergraph for video semantic detection [Elektronische Daten] [Yongzhao Zhan, Jiayao Sun, Dejiao Niu, Qirong Mao, Jianping Fan] Semantic categorization for the complex videos is an ambiguous task. The semi-supervised learning method based on hypergraph model can achieve multi-semantics labels, but a hypergraph model is sensitive to the radius parameter when it is constructed and the number of vertices belonging to a hyperedge is fixed. In this paper, a semi-supervised incremental learning method based on adaptive probabilistic hypergraph for video semantic detection is presented. In the probabilistic hypergraph modeling, a formula is presented as a measurement to adaptively decide whether a vertex is belonged to a hyperedge or not. The model has high robustness and can overcome the defect of fixed number of vertices belonging to the same hyperedge in the traditional probabilistic hypergraph model. In the semi-supervised incremental learning process, a threshold is defined, which is used to judge whether unlabeled sample can be added into the modeling, in order that the model learning result for unlabeled samples has high certainty. The experimental results show that our method can improve the model generalization ability and utilize the unlabeled samples effectively. In the aspects of recall rate and precision rate for semantic video concept detection from complex videos, our proposed method outperforms the compared methods. Springer Science+Business Media New York, 2014 Adaptive probabilistic hypergraph nationallicence Semi-supervised learning nationallicence Incremental learning nationallicence Video semantic detection nationallicence Zhan Yongzhao School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, China aut Sun Jiayao School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, China aut Niu Dejiao School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, China aut Mao Qirong School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, China aut Fan Jianping Department of Computer Science, UNC-Charlotte, 28223, Charlotte, NC, USA aut Multimedia Tools and Applications Springer US; http://www.springer-ny.com 74/15(2015-08-01), 5513-5531 1380-7501 74:15<5513 2015 74 11042 https://doi.org/10.1007/s11042-014-1866-9 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s11042-014-1866-9 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Zhan Yongzhao School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, China aut NATIONALLICENCE 700 1- Sun Jiayao School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, China aut NATIONALLICENCE 700 1- Niu Dejiao School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, China aut NATIONALLICENCE 700 1- Mao Qirong School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, China aut NATIONALLICENCE 700 1- Fan Jianping Department of Computer Science, UNC-Charlotte, 28223, Charlotte, NC, USA aut NATIONALLICENCE 773 0- Multimedia Tools and Applications Springer US; http://www.springer-ny.com 74/15(2015-08-01), 5513-5531 1380-7501 74:15<5513 2015 74 11042