Hierarchical Two-pathway Autoencoders Neural Networks for Skyline Context Conceptualization

Abstract : In this paper, we proposed a novel hierarchical two-pathway autoencoders architecture to transform a local information based on skyline scene representation, into non-linear space. The first pathway is intended for the transformation of the geometric features extracted from the horizon line. The second pathway is applied after the first one to joint the color information under the skyline to the transformed geometric features, and to get the landscape context conceptualization. To evaluate our suggested system, we constructed the SKYLINEScene database containing 2000 images of rural and urban landscapes, with a high degree of diversity. In order to investigate the performance of our HTANN-Skyline, many experiments were carried out using this new database. Our approach shows its robustness in Skyline context conceptualization and enhances the classification rates by 1% compared to the AlexNet architecture; and by more than 10% compared to the hand-crafted approaches based on global and local features. 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.
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Submitted on : Thursday, June 13, 2019 - 1:46:02 PM
Last modification on : Wednesday, November 20, 2019 - 3:20:24 AM

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  • HAL Id : hal-02072150, version 1

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Ameni Sassi, Wael Ouarda, Chokri Ben Amar, Serge Miguet. Hierarchical Two-pathway Autoencoders Neural Networks for Skyline Context Conceptualization. International Journal of Information and Decision Sciences, In press, pp.1 - 26. ⟨hal-02072150⟩

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