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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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: Campilho, A., Karray, F., ter Haar Romeny, B. (eds) Image Analysis and Recognition. ICIAR 2018. Lecture Notes in Computer Science(), vol 10882. Springer, Cham. https://doi.org/10.1007/978-3-319-93000-8_46
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