Fuzzy Latent-Dynamic Conditional Neural Fields for Gesture Recognition in Video
With the explosion of data on the internet led to the presence of the big data era, so it requires data processing in order to get the useful information. One of the challenges is the gesture recognition the video processing. Therefore, this study proposes Latent-Dynamic Conditional Neural Fields and compares with the other family members of Conditional Random Fields. To improve the accuracy, these methods are combined by using Fuzzy Clustering. From the result, it can be concluded that the performance of Latent-Dynamic Conditional Neural Fields are lower than Conditional Neural Fields but higher than the Conditional Random Fields and Latent-Dynamic Conditional Random Fields. Also, the combination of Latent-Dynamic Conditional Neural Fields and Fuzzy C-Means Clustering has the highest. This evaluation is tested in a temporal dataset of gesture phase segmentation.
Byung-Jun Yoon. 2009. Hidden Markov Models and their Applications in Biological Sequence Analysis. Current Genomics vol.10 page 402-415
C. Spampinato, S. Palazzo. 2012. Hidden Markov Models for Detecting Anomalous Fish Trajectories in Underwater Footage. 2012 IEEE International Workshop on Machine Learning for Signal Processing, Santander, Spain.
Martin F. Lambert, Julian P. Whiting, Andrew V. Metcalfe. 2003. A Non-parametric Hidden Markov Model for Climate State Identification. Hydrology and Earth System Sciences, 7(5), 652-667.
Ben Cooper, and Marc Lipsitch. 2004. The Analysis of Hospital Infection Data Using Hidden Markov Models. Biostatistics(2004),5,2,Pp.223–237.
Zhang, S., 2012. Fuzzy-based latent-dynamic conditional random fields for continuous gesture recognition. Optical Engineering, 51(6), p.067202.
John D. Lafferty, Andrew McCallum, Fernando C. N. Pereira. 2001. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence. In ICML 2001.
Jian Peng, Liefeng Bo, and Jinbo Xu. Conditional neural fields. In Proceedings of Neural Information Processing Systems (NIPS). 2009.
Levesque, J.C., Morency, L.P. and Gagné, C., “Sequential emotion recognition using Latent-Dynamic Conditional Neural Fields”, in Proc. 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, Shanghai, 2013, 1–6.
Madeo, R. C. B. ; Wagner, P. K. ; PERES, S. M.. A Review of Temporal Aspects of Hand Gesture Analysis Applied to Discourse Analysis and Natural Conversation. International Journal of Computer Science and Information Technology, v. 5, p. 1-20, 2013b.
Tamburini, F., Bertini, C., & Bertinetto, P. M. (2014). Prosodic prominence detection in Italian continuous speech using probabilistic graphical models. In Proceedings of Speech Prosody (SP-2014), Dublin, Ireland, pp. 285–289.
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