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

• Published in: International journal of intelligent systems and applications Vol. 11; no. 4; pp. 14 - 25
• Main Authors: Sassi, Ameni, Ouarda, Wael, Amar, Chokri Ben, Miguet, Serge
• Format: Journal Article
• Language: English
• Published: Hong Kong Modern Education and Computer Science Press 08.04.2019
• Subjects: Artificial neural networks; Computer vision; Learning; Scene analysis
• ISSN: 2074-904X; 2074-9058
• DOI: 10.5815/ijisa.2019.04.02

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.