Wael Ouarda

Work place: REGIM-Lab.: REsearch Groups in Intelligent Machines, University of Sfax, ENIS, BP 1173, 3038, Sfax, Tunisia

E-mail: wael.ouarda@ieee.org


Research Interests: Information Security, Information Systems, Combinatorial Optimization


Wael Ouarda received a Master Degree in Computer Science: Knowledge and Decision from the INSA Lyon in France in 2010. He is now a PhD in Research groups on Intelligent Machines from the National School of Engineers of Sfax. His current research interests include Soft Biometrics, Information Fusion, SOA Approach for IT and Optimization Patterns.

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

By Ameni Sassi Wael Ouarda Chokri Ben Amar Serge Miguet

DOI: https://doi.org/10.5815/ijisa.2019.04.02, Pub. Date: 8 Apr. 2019

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.

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