  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 a ground terrain image, and model the extent of
texture and shape information locally. Then, the order-less texture information
and ordered shape information are encoded in a patch-wise manner, which is
utilized by intra-domain message passing module to make every patch aware of
each other for rich feature learning. Next, the Extent-of-Texture (EoT) Guided
Inter-domain Message Passing module combines the extent of texture and shape
information with the encoded texture and shape information in a patch-wise
fashion for sharing knowledge to balance out the order-less texture information
with ordered shape information. Further, Bilinear model generates a pairwise
correlation between the order-less texture information and ordered shape
information. Finally, the ground-terrain image classification is performed by a
fully connected layer. The experimental results indicate superior performance
of the proposed model over existing state-of-the-art techniques on publicly
available datasets like DTD, MINC and GTOS-mobile.
