Open AccessA Novel Texture Descriptor: Circular Parts Local Binary Pattern
Author(s) -
Ibtissam Al Saidi,
Mohammed Rziza,
Johan Debayle
Publication year - 2021
Publication title -
image analysis and stereology
Language(s) - English
Resource type - Journals
eISSN - 1854-5165
pISSN - 1580-3139
DOI - 10.5566/ias.2580
Subject(s) - local binary patterns , pattern recognition (psychology) , artificial intelligence , pixel , curse of dimensionality , texture (cosmology) , mathematics , binary number , support vector machine , benchmark (surveying) , computer science , image (mathematics) , histogram , computer vision , geography , arithmetic , geodesy
Local Binary Pattern (LBP) are considered as a classical descriptor for texture analysis, it has mostly been used in pattern recognition and computer vision applications. However, the LBP gets information from a restricted number of local neighbors which is not enough to describe texture information, and the other descriptors that get a large number of local neighbors suffer from a large dimensionality and consume much time. In this regard, we propose a novel descriptor for texture classification known as Circular Parts Local Binary Pattern (CPLBP) which is designed to enhance LBP by extending the area of neighborhood from one to a region of neighbors using polar coordinates that permit to capture more discriminating relationships that exists amongst the pixels in the local neighborhood which increase efficiency in extracting features. Firstly, the circle is divided into regions with a specific radius and angle. After that, we calculate the average gray-level value of each part. Finally, the value of the center pixel is compared with these average values. The relevance of the proposed idea is validate in databases Outex 10 and 12. A complete evaluation on benchmark data sets reveals CPLBP's high performance. CPLBP generates the score of 99.95 with SVM classification.