Publication: Local spatial information with bag-of-visual-words model via graph-based representation for texture classification
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Issued Date
2020
Resource Type
File Type
application/pdf
ISSN
13494198
Other identifier(s)
2-s2.0-85089852913
Rights Holder(s)
มหาวิทยาลัยศรีนครินทรวิโรฒ
Bibliographic Citation
International Journal of Innovative Computing, Information and Control. Vol 16, No.5 (2020), p.1611-1621
Suggested Citation
Thewsuwan S., Horio K. Local spatial information with bag-of-visual-words model via graph-based representation for texture classification. International Journal of Innovative Computing, Information and Control. Vol 16, No.5 (2020), p.1611-1621. doi:10.24507/ijicic.16.05.1611 Retrieved from: https://hdl.handle.net/20.500.14740/4377
Author(s)
Abstract
This paper proposes an enhanced feature descriptor for texture classification through graph-based representation. Searching the meaningful texture descriptor is a crucial process in pattern analysis and applications. Graph theory is a model-based approach that applies to texture analysis with outstanding results. Therefore, to develop feature descriptors that are robust against many variations images collected from random viewpoints, change in scale, and illumination remains a challenge for researchers. In this work, we propose an Automatically Local Spatial Pattern Mapping (LSPMAuto ) method based on the spatial-BoVW model that can extract local and global features information from the spatial arrangement of image pixels. The proposed approach is evaluated by using three different texture databases: Brodatz, UIUC, and Outex. The experimental results show that the proposed method can achieve highly discriminant descriptors superior to the other methods. © 2020, ICIC International. All rights reserved.