[1] UEKI K, KOBAYASHI T. Object detection oriented feature pooling for video semantic indexing[C]//Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. Setúbal:SciTePress, 2017, 5:44-51. [2] KIKUCHI K, UEKI K, OGAWA T, et al. Video semantic indexing using object detection-derived features[C]//Proceedings of the 24th European Signal Processing Conference. Piscataway, NJ:IEEE, 2016:1288-1292. [3] QUEMY A, JAMROG K, JANISZEWSKI M. Unsupervised video semantic partitioning using IBM Watson and topic modelling[C]//Proceedings of the Workshops of the EDBT/ICDT 2018 Joint Conference. Piscataway, NJ:IEEE, 2018:44-49. [4] SHELHAMER E, RAKELLY K, HOFFMAN J, et al. Clockwork convnets for video semantic segmentation[C]//Proceedings of the 14th European Conference on Computer Vision, LNCS 9915. Berlin:Springer, 2016:852-868. [5] BULL L, WORDEN K, MANSON G, et al. Active learning for semi-supervised structural health monitoring[J]. Journal of Sound and Vibration, 2018, 437:373-388. [6] ZHOU Z-H. Ensemble Methods:Foundations and Algorithms[M]. 1st ed. Boca Raton, FL:Chapman & Hall, 2012:47-66. [7] JANG W D, KIM C-S. Semi-supervised video object segmentation using multiple random walkers[C]//Proceedings of the 27th British Machine Vision Conference. Guildford, UK:BMVA Press, 2016:57.1-57.13. [8] KUMAR V, NAMBOODIRI A, JAWAHAR C V. Semi-supervised annotation of faces in image collection[J]. Signal, Image and Video Processing, 2018, 12(1):141-149. [9] 景陈勇,詹永照,姜震.基于混合式协同训练的人体动作识别算法研究[J].计算机科学,2017,44(7):275-278. (JING C Y, ZHAN Y Z, JIANG Z. Research on action recognition algorithm based on hybrid cooperative training[J]. Computer Science, 2017, 44(7):275-278.) [10] WANG X, SONG H, CUI H. Pedestrian abnormal event detection based on multi-feature fusion in traffic video[J]. Optik, 2018, 154:22-32. [11] LI P, WANG H. Video semantic classification based on ELM and multi-features fusion[C]//Proceedings of the 2014 International Conference on Network Security and Communication Engineering. Leiden:CRC Press, 2015:305-308. [12] 严云洋, 杜静, 高尚兵, 等. 融合多特征的视频火焰检测[J]. 计算机辅助设计与图形学学报, 2015, 27(3):433-440. (YAN Y Y, DU J, GAO S B, et al. Video flame detection based on fusion of multi-feature[J]. Journal of Computer-Aded Design & Computer Graphics, 2015, 27(3):433-440.) [13] 蒋鹏, 秦小麟. 一种基于多特征的视频人物聚类方法[J].计算机科学,2008,35(5):240-242,245. (JIANG P, QIN X L. Automated person indexing in video[J]. Computer Science, 2008, 35(5):240-242, 245.) [14] 陈芬,赖茂生.多特征视频分类挖掘实验研究[J].现代图书情报技术,2012,28(5):76-80. (CHEN F, LAI M S. Video classification using multiple features[J]. New Technology of Library and Information Service, 2012, 28(5):76-80.) [15] MARTÍN R, MARTÍNEZ J M. A semi-supervised system for players detection and tracking in multi-camera soccer videos[J]. Multimedia Tools & Applications, 2014, 73(3):1617-1642. [16] ZHAN Y, SUN J, NIU D, et al. A semi-supervised incremental learning method based on adaptive probabilistic hypergraph for video semantic detection[J]. Multimedia Tools & Applications, 2015, 74(15):5513-5531. [17] MISRA I, SHRIVASTAVA A, HEBERT M. Watch and learn:semi-supervised learning of object detectors from videos[C]//Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2015:3593-3602. [18] YANG Y, CHEN S. Ensemble learning from imbalanced data set for video event detection[C]//Proceedings of the 16th IEEE International Conference on Information Reuse and Integration. Piscataway, NJ:IEEE, 2015:82-89. [19] MITREA C A, CARATA S, IONESCU B, et al. Ensemble-based learning using few training samples for video surveillance scenarios[C]//Proceedings of the 5th International Conference on Image Processing, Theory, Tools and Applications. Piscataway, NJ:IEEE, 2015:93-98. [20] SHI W, JIANG M. Face recognition based on multi-view:ensemble learning[C]//Proceedings of the 1st Chinese Conference on Pattern Recognition and Computer Vision, LNCS 11258. Cham:Springer, 2018:127-136. [21] ZHANG Y, HUANG Q, MA X, et al. Using multi-features and ensemble learning method for imbalanced malware classification[C]//Proceedings of the 2016 IEEE Trustcom/BigDataSE/ISPA. Piscataway, NJ:IEEE, 2016:965-973. [22] ALBUKHANAJER W A, JIN Y, BRIFFA J A. Classifier ensembles for image identification using multi-objective Pareto features[J]. Neurocomputing, 2017, 238:316-327. [23] REDDY K K, SHAH M. Recognizing 50 human action categories of web videos[J]. Machine Vision and Applications, 2013, 24(5):971-981. [24] LIU D, SHYU M, ZHAO G. Spatial-temporal motion information integration for action detection and recognition in non-static background[C]//Proceedings of the 14th International Conference on Information Reuse and Integration. Washington, DC:IEEE Computer Society, 2013:626-633. [25] EVERTS I, GEMERT J C van, GEVERS T. Evaluation of color STIPs for human action recognition[C]//Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2013:2850-2857. |