dc.contributor.author | Alpaslan, N. and Hanbay, K. | |
dc.date.accessioned | 2021-04-08T12:06:29Z | |
dc.date.available | 2021-04-08T12:06:29Z | |
dc.date.issued | 2020 | |
dc.identifier | 10.1109/LSP.2020.2987474 | |
dc.identifier.issn | 10709908 | |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085516949&doi=10.1109%2fLSP.2020.2987474&partnerID=40&md5=79b83a4445810a78e75d97b84a533289 | |
dc.identifier.uri | http://acikerisim.bingol.edu.tr/handle/20.500.12898/3959 | |
dc.description.abstract | To enhance the weakness of Local Binary Pattern (LBP) and its state-of-the-art variants, this letter presents a new variant of the local concave microstructure pattern (LCvMSP). The proposed multi-scale shape index based texture descriptor is named as SI-LCvMSP. Contrarily to the original LBP and LCvMSP, SI-LCvMSP uses the shape index instead of the original texture image in the kernel function. The shape index is a differential calculation and it can be calculated from local second-order derivatives of texture images. It captures microstructure and macrostructure texture information mathematically. As textural features, we use multi-scale and multi-resolution shape index information as well as rotation-invariant uniform LBP. Thus, we obtain the discriminative feature representation schema to construct cross-scale joint coding. The proposed method has a high discriminability and is less sensitive to image transforms such as rotation and illumination. Experimental results show that the SI-LCvMSP descriptor can improve classification accuracy. © 1994-2012 IEEE. | |
dc.language.iso | English | |
dc.source | IEEE Signal Processing Letters | |
dc.title | Multi-Scale Shape Index-Based Local Binary Patterns for Texture Classification | |