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2024 | OriginalPaper | Buchkapitel

State-Of-The-Art Methods for Dynamic Texture Classification: A Comprehensive Review

verfasst von : Manal Benzyane, Mourade Azrour, Imad Zeroual, Said Agoujil

Erschienen in: Sustainable and Green Technologies for Water and Environmental Management

Verlag: Springer Nature Switzerland

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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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Titel: State-Of-The-Art Methods for Dynamic Texture Classification: A Comprehensive Review
verfasst von: Manal Benzyane
Mourade Azrour
Imad Zeroual
Said Agoujil
Verlag: Springer Nature Switzerland
Buch: Sustainable and Green Technologies for Water and Environmental Management
Print ISBN: 978-3-031-52418-9

Electronic ISBN: 978-3-031-52419-6

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-3-031-52419-6_1

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