Abstract
Dynamic texture classification has become an increasingly important research area due to the growing availability of video data and the need for efficient video analysis. Recently, deep learning models have demonstrated remarkable success in automatically classifying dynamic textures. This review provides a comprehensive and concise overview of recent advances in dynamic texture classification, with a particular focus on deep learning-based approaches. We survey the most popular deep learning architectures used for this task, highlighting key findings based on existing deep learning models and offering future research directions. Our review covers network architecture, evaluation criteria, datasets used in video classification, and provides an overview of deep learning methods.
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Benzyane, M., Azrour, M., Zeroual, I., Agoujil, S. (2024). State-Of-The-Art Methods for Dynamic Texture Classification: A Comprehensive Review. In: Azrour, M., Mabrouki, J., Guezzaz, A. (eds) Sustainable and Green Technologies for Water and Environmental Management. World Sustainability Series. Springer, Cham. https://doi.org/10.1007/978-3-031-52419-6_1
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