Neural Approach for Context Scene Image Classification based on Geometric, Texture and Color Information

Abstract : Revealing the context of a scene from low-level features representation , is a challenging task for quite a long time. The classification of landscapes scenes to urban and rural categories is a preliminary task for landscapes scenes understanding. Having a global idea about the scene context (rural or urban) before investigating its details, would be an interesting way to predict the content of that scene. In this paper, we propose a novel features representation based on skyline, colour and texture, transformed by a sparse coding using Stacked Auto-Encoder. To evaluate our proposed approach; we construct a new database called SKYLINEScene Database containing 2000 images of rural and urban landscapes with a high degree of diversity. Many experiments were carried out using this database. Our approach shows it robustness in landscapes scenes classification.
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Ameni Sassi, Wael Ouarda, Chokri Ben Amar, Serge Miguet. Neural Approach for Context Scene Image Classification based on Geometric, Texture and Color Information. Representation, analysis and recognition of shape and motion FroM Image data, RFIA, Dec 2017, Aussois, France. ⟨hal-01687973⟩

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