Work place: LIRIS, Université de Lyon, UMR CNRS 5202, Université Lumière Lyon 2, 5 av. Mendès-France, Bât C, N 123, 69676. Bron, Lyon, France
E-mail: serge.miguet@univ-lyon2.fr
Website:
Research Interests: Pattern Recognition, Image Compression, Image Manipulation, Image Processing
Biography
Serge Miguet graduated from the ENSIMAG (Grenoble, France) in 1988. He obtained a PhD from the INPG in 1990. He was an Assistant Professor at the ENS de Lyon, and a member of the LIP laboratory from 1991 to 1996. He received his Habilitation Diriger des Recherches from the Université Claude Bernard Lyon 1 in 1995. Since 1996, he is a full Professor in Computer Science
at the Université Lumière Lyon 2, and a member of the LIRIS laboratory, UMR CNRS 5205. His main research activities are devoted to models and tools for image processing, image analysis, shape recognition.
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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