Work place: REGIM-Lab.: REsearch Groups in Intelligent Machines, University of Sfax, ENIS, BP 1173, 3038, Sfax, Tunisia
E-mail: wael.ouarda@ieee.org
Website:
Research Interests: Information Security, Information Systems, Combinatorial Optimization
Biography
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.
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.
[...] Read more.Subscribe to receive issue release notifications and newsletters from MECS Press journals