Ordered Local Binary Pattern (OLBP) For Classification of Textures
Anil kumar Muthevi1, Ravi babu.Uppu2

1Anil kumar Muthevi, Research Scholar, Department of CSE, Acharya Nagarjuna University, India. Associate Professor, ACET, Surampalem , (AP), India.
2Dr Ravi babu Uppu, Principal & Professor, Department of CSE ,DRKCET, Hyderabad, (Telangana), India.

Manuscript received on 24 January 2019 | Revised Manuscript received on 30 March 2019 | Manuscript published on 30 January 2019 | PP: 243-247 | Volume-7 Issue-6, March 2019 | Retrieval Number: E2037017519©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Conventional Local Binary Pattern (LBP) methods follow the patterns whose rotations are lesser than two or certain limited numbers are called rotation invariant binary patterns. In the conventional rotational-invariant encoding method has disadvantage due to neglecting information of the some patterns by its process of encoding. It ignores the patterns when their spatial transition is greater than two for maintaining the rotation-invariant nature. But these disregarded patterns will plays crucial role and have very much more discriminative power. Here, the present study proposing a novel model called OLBP by changing (sorting) the order of consecutive binary patterns without disturbing the property of rotational invariance. The result observed by experiments indicates the proposed work shows better classification rate which is worked on the standard databases when compared to previous existing methods.
Keywords: Texture; Neighborhood pixel; Local Binary Pattern (LBP); Histogram; Rotational Invariance; Classification;
Scope of the Article: Classification