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Convolutional neural network on three orthogonal planes for dynamic texture classification

Andrearczyk, Vincent and Whelan, Paul F. orcid logoORCID: 0000-0001-9230-7656 (2017) Convolutional neural network on three orthogonal planes for dynamic texture classification. Pattern Recognition, 76 . pp. 36-49. ISSN 0031-3203

Dynamic Textures (DTs) are sequences of images of moving scenes that exhibit certain stationarity properties in time such as smoke, vegetation and fire. The analysis of DT is important for recognition, segmentation, synthesis or retrieval for a range of applications including surveillance, medical imaging and remote sensing. Deep learning methods have shown impressive results and are now the new state of the art for a wide range of computer vision tasks including image and video recognition and segmentation. In particular, Convolutional Neural Networks (CNNs) have recently proven to be well suited for texture analysis with a design similar to a filter bank approach. In this paper, we develop a new approach to DT analysis based on a CNN method applied on three orthogonal planes x y, xt and yt . We train CNNs on spatial frames and temporal slices extracted from the DT sequences and combine their outputs to obtain a competitive DT classifier. Our results on a wide range of commonly used DT classification benchmark datasets prove the robustness of our approach. Significant improvement of the state of the art is shown on the larger datasets.
Item Type:Article (Published)
Uncontrolled Keywords:computer vision; deep learning; image analysis; Convolutional Neural Networks; Texture Classification; Dynamic Texture
Subjects:Computer Science > Image processing
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Electronic Engineering
Official URL:https://doi.org/10.1016/j.patcog.2017.10.030
Copyright Information:© Elsevier
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
ID Code:22093
Deposited On:27 Oct 2017 13:00 by Paul Whelan . Last Modified 23 Oct 2019 03:30

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