Abstract
We present a novel learning algorithm for Human action recognition and categorization. Our purpose here is to develop a Riemannian Averaged Fixed-Point estimation algorithm (RA-FP) for learning the multivariate generalized Gaussian mixture model’s parameters (MGGMM). Experiments in a large datasets of human action images have shown the merits of our approach.
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CITATION STYLE
Najar, F., Bourouis, S., Zaguia, A., Bouguila, N., & Belghith, S. (2018). Unsupervised Human Action Categorization Using a Riemannian Averaged Fixed-Point Learning of Multivariate GGMM. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10882 LNCS, pp. 408–415). Springer Verlag. https://doi.org/10.1007/978-3-319-93000-8_46
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