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Title: Modeling extent-of-texture information for ground terrain recognition
Authors: Ghose S.
Chowdhury P.N.
Pratim Roy, Partha
Pal U.
Published in: Proceedings - International Conference on Pattern Recognition
25th International Conference on Pattern Recognition, ICPR 2020
Abstract: 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 model1 over existing state-of-the-art techniques on DTD, MINC and GTOS-mobile datasets. © 2020 IEEE
Citation: Proceedings - International Conference on Pattern Recognition (2020): 4766-4773
Issue Date: 2020
Publisher: Institute of Electrical and Electronics Engineers Inc.
Keywords: Image texture
Message passing
Pattern recognition
Context information
Feature extractor
Pairwise correlation
Sharing knowledge
Spatial informations
State-of-the-art techniques
Texture components
Texture information
Classification (of information)
ISBN: 9.78173E+12
ISSN: 10514651
Author Scopus IDs: 57209826260
Author Affiliations: Ghose, S., Institute of Engineering and Management, Kolkata, India
Chowdhury, P.N., Indian Statistical Institute, Kolkata, India
Roy, P.P., Indian Institute of Technology, Roorkee, India
Pal, U., Indian Statistical Institute, Kolkata, India
Appears in Collections:Conference Publications [CS]

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