Show simple item record

dc.contributor.authorAlpaslan, N. and Hanbay, K.
dc.date.accessioned2021-04-08T12:06:29Z
dc.date.available2021-04-08T12:06:29Z
dc.date.issued2020
dc.identifier10.1109/LSP.2020.2987474
dc.identifier.issn10709908
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85085516949&doi=10.1109%2fLSP.2020.2987474&partnerID=40&md5=79b83a4445810a78e75d97b84a533289
dc.identifier.urihttp://acikerisim.bingol.edu.tr/handle/20.500.12898/3959
dc.description.abstractTo 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.isoEnglish
dc.sourceIEEE Signal Processing Letters
dc.titleMulti-Scale Shape Index-Based Local Binary Patterns for Texture Classification


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record