Skip to Main content Skip to Navigation
Journal articles

Sky-CNN: A CNN-based Learning Approach for Skyline Scene Understanding

Abstract : 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.
Complete list of metadata

Cited literature [35 references]  Display  Hide  Download
Contributor : Serge Miguet <>
Submitted on : Sunday, February 9, 2020 - 5:19:58 PM
Last modification on : Tuesday, June 1, 2021 - 2:08:09 PM
Long-term archiving on: : Sunday, May 10, 2020 - 1:04:23 PM


Publisher files allowed on an open archive





Ameni Sassi, Wael Ouarda, Chokri Ben Amar, Serge Miguet. Sky-CNN: A CNN-based Learning Approach for Skyline Scene Understanding. International Journal of Intelligent Systems and Applications, 2019, 4, pp.14 - 25. ⟨10.5815/ijisa.2019.04.02⟩. ⟨halshs-02471883⟩



Record views


Files downloads