Sky-CNN: a CNN-based learning approach for skyline scene understanding
Автор: Ameni Sassi, Wael Ouarda, Chokri Ben Amar, Serge Miguet
Журнал: International Journal of Intelligent Systems and Applications @ijisa
Статья в выпуске: 4 vol.11, 2019 года.
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Skyline scenes are a scientific matter of interest for some geographers and urbanists. These scenes have not been well-handled in computer vision tasks. Understanding the context of a skyline scene could refer to approaches based on hand-crafted features combined with linear classifiers; which are somewhat side-lined in favor of the Convolutional Neural Networks based approaches. In this paper, we proposed a new CNN learning approach to categorize skyline scenes. The proposed model requires a pre-processing step enhancing the deep-learned features and the training time. To evaluate our suggested system; we constructed the SKYLINEScene database. This new DB contains 2000 images of urban and rural landscape scenes with a skyline view. In order to examine the performance of our Sky-CNN system, many fair comparisons were carried out using well-known CNN architectures and the SKYLINEScene DB for tests. Our approach shows it robustness in Skyline context understanding and outperforms the hand-crafted approaches based on global and local features.
Convolutional Neural Network, deep learning, scene categorization, skyline, features representation, deep learned features
Короткий адрес: https://sciup.org/15016584
IDR: 15016584 | DOI: 10.5815/ijisa.2019.04.02
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