Modeling Extent-of-Texture Information for Ground Terrain Recognition

Ground Terrain Recognition is a difficult task as the context information varies significantly over the regions of a ground terrain image. In this paper, we propose a novel approach towards ground-terrain recognition via modeling the Extent-of-Texture information to establish a balance between the order-less texture component and ordered-spatial information locally. At first, the proposed method uses a CNN backbone feature extractor network to capture meaningful information of ground terrain images, and model the extent of texture and shape information locally. Then, it encodes order-less texture information and ordered shape information in a patch-wise manner, and utilizes an intra-domain message passing mechanism to make every patch aware of each other for rich feature learning. Next, the model combines the extent of texture information with the encoded texture information and the extent of shape information with the encoded shape information patch-wise and then exploit Extent of texture (EoT) Guided Inter-domain Message passing module for sharing knowledge about the opposite domain to balance out the order-less texture information with ordered shape information. Finally, Bilinear model outputs a pairwise correlation between the order-less texture information and ordered shape information, and classifier classifies the ground terrain image efficiently. The experimental results indicate the superior performance of the proposed model
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The source code of the proposed system is publicly available at https://github.com/ShuvozitGhose/Ground-Terrain-EoT over existing state-of-the-art techniques on DTD, MINC and GTOS-mobile datasets.