Odetallah A D, Agaian S S. Human Visual System-Based Smoking Event Detection // Proc of SPIE 8406: The Mobile Multimedia /Image Processing, Security, and Applications. Baltimore, USA, 2012: 4344-4347
[2]
Bien T L, Lin C H. Detection and Recognition of Indoor Smoking Events//Proc of the 15th International Conference on Machine Vision. Wuhan, China, 2012. DOI:10.1117/12.2020967
[3]
Inoue H, Tanake T. Image-Based Smoke Detection with k-Subspaces Clustering//Proc of RISP International Workshop on Nonlinear Circuits and Signal Processing. Hawaii, USA, 2009:321-324
[4]
Wu P, Hsieh J W, Cheng J C, et al. Human Smoking Event Detection Using Visual Interaction Clues // Proc of the 20th International Conference on Pattern Recognition. Istanbul, Turkey, 2010: 4344-4347
[5]
Chang S F, Sikora T, Purl A. Overview of the MPEG-7 Standard. IEEE Trans on Circuits and Systems for Video Technology, 2001, 11(6): 688-695
[6]
Sikora T. The MPEG-7 Visual Standard for Content Description-An Overview. IEEE Trans on Circuits and Systems for Video Technology, 2001, 11(6): 696-702
[7]
Wang T, Liu Y, Xie Z P. Flutter Analysis Based Video Smoke Detection. Journal of Electronics & Information Technology, 2011, 33(5): 1024-1029 (in Chinese) (王 涛,刘 渊,谢振平.一种基于飘动性分析的视频烟雾检测新方法.电子与信息学报, 2011, 33(5): 1024-1029)
[8]
Yuan F N. A Fast Accumulative Motion Orientation Model Based on Integral Image for Video Smoke Detection. Pattern Recognition Lett-ers, 2008, 29(7): 925-932
[9]
Kira K, Rendell L A. A Practical Approach to Feature Selection // Proc of the 9th International Workshop on Machine Learning. Aberdeen, UK, 1992: 249-256
[10]
Zhang X, Deng Z H, Wang S T, et al. Maximum Entropy Relief Feature Weighting. Journal of Computer Research and Development, 2011, 48(6): 1038-1048 (in Chinese)(张 翔,邓赵红,王士同,等.极大熵Relief特征加权.计算机研究与发展, 2011, 48(6): 1038-1048)
[11]
Battiti R. Using Mutual Information for Selecting Features in Supervised Neural Net Learning. IEEE Trans on Neural Networks, 1994, 5(4): 537-550
[12]
Kwak N, Choi C H. Input Feature Selections for Classification Problems. IEEE Trans on Neural Networks, 2002, 13(1): 143-159
[13]
Peng X J, Wang Y F. A Bi-Fuzzy Progressive Transductive Support Vector Machine Algorithm. Pattern Recognition and Artificial Intelligence, 2009, 22(4): 560-566 (in Chinese)(彭新俊,王翼飞.双模糊渐进直推式支持向量机算法. 模式识别与人工智能, 2009, 22(4): 560-566)
