{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T16:15:03Z","timestamp":1781108103034,"version":"3.54.1"},"reference-count":21,"publisher":"IGI Global Scientific Publishing","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,4,1]]},"abstract":"<p>Texture classification is an important issue in digital image processing and the Local Binary pattern (LBP) is a very powerful method used for analysing textures. LBP has gained significant popularity in texture analysis world. However, LBP method is very sensitive to noise and unable to capture the macrostructure information of the image. To address its limitation, some variants of LBP have been defined. In this chapter, the texture classification performance of LBP has been compared with the five latest high-performance LBP variants, like Centre symmetric Local Binary Pattern (CS-LBP), Orthogonal Combination of Local Binary Patterns (OC LBP), Rotation Invariant Local Binary Pattern (RLBP), Dominant Rotated Local Binary Pattern (DRLBP) and Median rotated extended local binary pattern (MRELBP). This was by using the standard images Outex_TC_0010 dataset. From the experimental results it is concluded that DRLBP and MRELBP are the best methods for texture classification.<\/p>","DOI":"10.4018\/ijismd.2017040103","type":"journal-article","created":{"date-parts":[[2018,2,1]],"date-time":"2018-02-01T10:36:46Z","timestamp":1517481406000},"page":"45-56","source":"Crossref","is-referenced-by-count":0,"title":["Comparative Analysis of Texture Classification Using Local Binary Pattern and Its Variants"],"prefix":"10.4018","volume":"8","author":[{"given":"Richa","family":"Sharma","sequence":"first","affiliation":[{"name":"Punjabi University, Patiala, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Madan","family":"Lal","sequence":"additional","affiliation":[{"name":"Punjabi University, Patiala, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"issue":"9","key":"IJISMD.2017040103-0","doi-asserted-by":"crossref","first-page":"1705","DOI":"10.1109\/TPAMI.2009.155","article-title":"A Robust Local Image Descriptor.","volume":"32","author":"J.Chen","year":"2010","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"IJISMD.2017040103-1","doi-asserted-by":"crossref","unstructured":"Das, S., & Jena, U. 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D., & Kumar, D. S. P. (2013). Vessels Segmentation in Diabetic Retinopathy by Adaptive Medina Thresholding. International Journal of Science and Technoledge, (1), 17-22."},{"key":"IJISMD.2017040103-13","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR.2002.1044854"},{"issue":"7","key":"IJISMD.2017040103-14","doi-asserted-by":"crossref","first-page":"971","DOI":"10.1109\/TPAMI.2002.1017623","article-title":"Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Pattern.","volume":"24","author":"T.Ojala","year":"2002","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"IJISMD.2017040103-15","doi-asserted-by":"publisher","DOI":"10.1016\/0031-3203(95)00067-4"},{"key":"IJISMD.2017040103-16","unstructured":"Patil, H. S., & Patil, S. S. (2013). Rotation and Illumination Invariant Texture Classification for Image Retrieval Using Local Binary Pattern. 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