Serge Miguet

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



Research Interests: Pattern Recognition, Image Compression, Image Manipulation, Image Processing


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.

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

By Ameni Sassi Wael Ouarda Chokri Ben Amar Serge Miguet

DOI:, 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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