Segmentation of Multi-Textured Images using Optimized Local Ternary Patterns

Abstract

Texture segmentation is a process of segmenting an image into differently textured regions. This paper is aimed at the segmentation of multitextured images by using Optimized Local Ternary Patterns (OLTP), a new texture model which is recently proposed for texture analysis. This paper uses unsupervised texture segmentation method with the application of optimized local ternary patterns for finding the dissimilarity of adjacent image regions during the segmentation process. The performance of this recently proposed texture measure OLTP is evaluated, compared with other texture models Texture Spectrum (TS) and Local Binary Patterns (LBP) and found to be the best.

Face Representation and Face Recognition using Optimized Local Ternary Patterns (OLTP)

“…Optimized Local Ternary Patterns (OLTP), a recent texture feature method which was already successfully applied for texture classification [18] and segmentation [19] is applied for face recognition in this paper. In OLTP, a texture image can be decomposed into a set of small units called patterns.…”