Texture Classification based on First Order Local Ternary Direction Patterns
Author(s) -
Mekala Srinivasa Rao,
V. Vijaya Kumar,
MHM Krishna Prasad
Publication year - 2017
Publication title -
international journal of image graphics and signal processing
Language(s) - English
Resource type - Journals
eISSN - 2074-9082
pISSN - 2074-9074
DOI - 10.5815/ijigsp.2017.02.06
Subject(s) - pixel , local binary patterns , pattern recognition (psychology) , ternary operation , binary number , computer science , invariant (physics) , artificial intelligence , texture (cosmology) , benchmark (surveying) , mathematics , algorithm , image (mathematics) , histogram , cartography , geography , arithmetic , programming language , mathematical physics
The local binary pattern (LBP) and local ternary pattern (LTP) are basically gray scale invariant, and they encode the binary/ ternary relationship between the neighboring pixels and central pixel based on their grey level differences and derives a unique code. These traditional local patterns ignore the directional information. The proposed method encodes the relationship between the central pixel and two of its neighboring pixel located in different angles (α, β) with different directions. To estimate the directional patterns, the present paper derived variation in local direction patterns in between the two derivates of first order and derived a unique First order –Local Direction variation pattern (FO-LDVP) code. The FO-LDVP evaluated the possible direction variation pattern for central pixel by measuring the first order derivate relationship among the horizontal and vertical neighbors (0 o Vs.90 o ; 90 o Vs. 180 o ; 180 o Vs.270 o ; 270 o Vs. 0 o ) and derived a unique code. The performance of the proposed method is compared with LBP, LTP, LBPv, TS and CDTM using the benchmark texture databases viz. Brodtaz and MIT VisTex. The performance analysis shows the efficiency of the proposed method over the existing methods.
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