A hierarchical representation for human action recognition in realistic scenes
Tóm tắt
Từ khóa
Tài liệu tham khảo
Bhushan K et al. (2017) A novel approach to defend multimedia flash crowd in cloud environment. Multimedia Tools and Applications 2017(3):1–31
Castrodad A, Sapiro G, Castrodad A et al (2012) Sparse modelling of human actions from motion imagery. Int J Comput Vis 100(1):1–15
Csurka G, Dance C, Fan L et al (2004) Visual Categorization with Bags of Keypoints. Workshop on Statistical Learning in Computer Vision(ECCV), pp.1–22
Ding C, Li T (2007) Adaptive Dimension Reduction Using Discriminant Analysis and K-means Clustering. Proceedings of the 24th International Conference on Machine learning, pp. 521–528
Dollar P, Rabaud V, Cottrell G et al (2005) Behaviour recognition via sparse spatio-temporal features. Proceedings of the International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, pp. 65–72
Garcica RH, Cozar JR, Guil N, Reyes EG, Sahli H (2017) Improving bag-of-visual-words model using visual n-grams for human action classification. Expert Syst Appl 92:182–191
Guha T, Ward RK (2012) Learning sparse representations for human action recognition. IEEE Trans Pattern Anal Mach Intell 34(8):1576–1588
Gupta B, Agrawal DP, Yamaguchi S (eds) (2016) Handbook of research on modern cryptographic solutions for computer and cyber security. IGI Global Publisher, USA
Gupta A, Kembhavi A, Davis LS et al (2009) Observing human-object interactions: using spatial and functional compatibility for recognition. IEEE Trans Pattern Anal Mach Intell 31(10):1775–1789
Gupta S, et al (2016) XSS-secure as a service for the platforms of online social network-based multimedia web applications in cloud. Multimedia Tools and Applications, pp.1–33
Gupta S et al (2017) Enhancing the browser-side context-aware sanitization of suspicious HTML5 code for halting the DOM-based XSS vulnerabilities in cloud. Int J Cloud Appl Comput 7(1):1–31
Ji S, Xu W, Yang M et al (2013) 3D convolutional neural networks for human action recognition. IEEE Trans Pattern Anal Mach Intell 35(1):221–231
Laptev I, Lindeberg T (2004) Velocity adaption of space-time interest points. Proceedings of the 17th International Conference on Pattern Recognition, pp.52–56
Laptev I, Lindeberg T (2013) Space-time interest points. Proceedings of the International Conference on Computer Vision, pp.432–439
Laptev I, Marszałek M, Schmid C, et al (2008) Learning realistic human actions from movies. Proceedings of the conference on computer vision and pattern recognition, pp. 1–8
Lazebnik S, Schmid C, Ponce J (2006) Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. IEEE Conference on Computer Vision and Pattern Recognition, pp. 2169–2178
Le QV, Zou WY, Yeung SY et al. (2011) Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis. In: IEEE Conference on Computer Vision and Pattern Recognition, pp 3361–3368. https://doi.org/10.1109/CVPR.2011.5995496
Li Y, Peng Z, Liang D et al (2016) Facial age estimation by using stacked feature composition and selection. Vis Comput 32(12):1525–1536
Li Y, Wang G, Lin N, Wang Q (2017) Distance metric optimization driven convolutional neural network for age invariant face recognition. Pattern Recogn 75:51–62
Liu J, Luo J, Shah M (2009) Recognizing realistic actions from videos in the Wild. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp.1996–2003
Liu J, Luo J, Shah M (2009) Recognizing realistic actions from videos in the wild. Proceedings of the Computer Vision and Pattern Recognition, pp. 1996–2003
Liu J, Yang Y, Saleemi I et al (2012) Learning semantic features for action recognition via diffusion maps. Comput Vis Image Underst 116(3):361–377
Marszalek M, Laptev I, Schmid C (2009) Actions in context. IEEE Conference on Computer Vision and Pattern Recognition, pp. 2929–2936
Niebles JC, Fei-Fei L (2007) A hierarchical model of shape and appearance for human action classification. Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), pp.1–8
Rodriguez MD, Ahmed J, Shah M (2008) Action MACH: A spatio-temporal maximum average correlation height filter for action recognition. in 26th IEEE Conference on Computer Vision and Pattern Recognition(CVPR), 2008:1–8
Schuldt C, Laptev I, Caputo B (2004) Recognizing human actions: a local SVM approach. Proceedings of the International Conference on Pattern Recognition (ICPR), pp. 3:32–36
Christian Schuldt, Ivan Laptev, Barbara Caputo (2004) Recognizing human actions: a local SVM approach. Proceedings of the International Conference on Pattern Recognition, pp. 3:32–36
Sun J, Wu X, Yan S, et al (2009) Hierarchical spatio-temporal context modelling for action recognition. Proceedings of the Conference on Computer Vision and Pattern Recognition, pp. 1–8
Sun Q, Liu H, Ma L, Zhang T (2016) A novel hierarchical bag-of-words model for compact action representation. Neurocomputing 174:722–732
Wang H, Ullah MM, Klaser A, et al (2010) Evaluation of local spatio-temporal features for action recognition. British Machine Vision Conference, pp. 1–11
Wu J, Thompson J, Zhang H, Kilper DC (2014) Green communications and computing networks [series editorial]. IEEE Commun Mag 52(11):102–103
Wu J, Thompson J, Zhang H, Kilper DC (2015) Green communications and computing networks [series editorial]. IEEE Commun Mag 53(11):214–215
Wu J, Guo S, Li J, Zeng D (2016) Big data meet green challenges: big data toward green applications. IEEE Syst J 10(3):888–900
Wu J, Guo S, Li J, Zeng D (2016) Big Data Meet Green Challenges: Greening Big Data. IEEE Syst J 10(3):873–887
Zain A, Mohammed A et al (2015) Multi-cloud data management using Shamir's secret sharing and quantum byzantine agreement schemes. Int J Cloud Appl Comput 5(3):35–52