Variational Em Inference Algorithm For Gaussian Process Classification Model With Multiclass And Its Application To Human Action Classification

• Main Authors: Wanhyun Cho, Soonja Kang, Sangkyoon Kim, Soonyoung Park
• Format: Text
• Language: English
• Published: Zenodo 2015
• Subjects: Bayesian rule; Gaussian process classification model with multiclass; Gaussian process prior; human action classification; laplace approximation; variational EM algorithm.; Laplace; Rasmussen; laptev
• Online Access: https://dx.doi.org/10.5281/zenodo.1110274; https://zenodo.org/record/1110274

In this paper, we propose the variational EM inference algorithm for the multi-class Gaussian process classification model that can be used in the field of human behavior recognition. This algorithm can drive simultaneously both a posterior distribution of a latent function and estimators of hyper-parameters in a Gaussian process classification model with multiclass. Our algorithm is based on the Laplace approximation (LA) technique and variational EM framework. This is performed in two steps: called expectation and maximization steps. First, in the expectation step, using the Bayesian formula and LA technique, we derive approximately the posterior distribution of the latent function indicating the possibility that each observation belongs to a certain class in the Gaussian process classification model. Second, in the maximization step, using a derived posterior distribution of latent function, we compute the maximum likelihood estimator for hyper-parameters of a covariance matrix necessary to define prior distribution for latent function. These two steps iteratively repeat until a convergence condition satisfies. Moreover, we apply the proposed algorithm with human action classification problem using a public database, namely, the KTH human action data set. Experimental results reveal that the proposed algorithm shows good performance on this data set. : {"references": ["C. E. Rasmussen, and C. K. I. Williams, \"Gaussian Processes for\nMachine Learning,\" MIT Press, 2006.", "H. Nicklisch, and C. E. Rasmussen, \"Approximation for Binary Gaussian\nprocess Classification,\" JMLR, 2008, pp. 2035-75.", "A. B. Chan, and D. Dong, \"Generalized Gaussian process model,\" IEEE\nConf. on Computer Vision and Pattern Recognition, Colorado Spring,\n2011.", "A. C. Chan, \"Multivariate generalized Gaussian process models,\" eprint\narXiv: 1311.0360, 2013.", "H. Kim, and Z. Ghahramani, \"Bayesian Gaussian Process Classification\nwith the EM-EP algorithm,\" IEEE Trans. on PAMI, vol. 28, no. 12, pp\n1948-1959, 2006.", "C. E. Rasmussen, and H. Nickisch, The GPML Toolbox version 3.4,\ngaussianprocess.org.", "L. Raskin, E. Rivlin, and M. Rudzsky, \"Using Gaussian Processes for\nHuman tracking and Action Classification\", ISVC 2007, Part 1, LNCS\n4841, pp 36-45, 2007.", "H. Zhou, L. Wang, D. Sutter, \"Human action recognition by\nfearture-reduced Gaussian process classification\", Pattern Recognition\nLetters, v0l. 30, pp 1059-1065, 2009.", "Q. Zhao, L. Zhang, A. Cjchocki, \"A Tensor-Variate Gaussian Process for\nClassification of Multidinensional Structured Data\", Proceeding of the\nTwenty-Seventh AAAI Conference on Artificial Intelligence, pp\n1041-1047, 2013.\n[10] I. Laptev, M. Marszalek, C. Schmid, and B. Rozenfeld, \"Learning\nrealistic human actions from movies,\" in CVPR 2008.\n[11] K. Mikolajczyk and H. Uemura. \"Action recognition with\nmotion-appearance vocabulary forest,\" CVPR, 2008.\n[12] J. Yuan, Z. Liu, and Y. Wu, \"Discriminative Subvolume Search for\nEfficient Action Detection,\" CVPR, 2009.\n[13] M. B. Kaaniche and F. Bremond, \"Gesture Recognition by Learning\nLocal Motion Signatures,\" In CVPR, 2010.\n[14] A. Kovashka and K. Grauman, \"Learning a Hierarchy of Discriminative\nSpace-Time Neighborhood Features for Human Action Recognition,\" In\nCVPR, 2010.\n[15] J. Yin and Y. Meng, \"Human Activity Recognition in Video using a\nHierarchical Probabilistic Latent Model,\" In CVPR, 2010."]}