Completed local ternary pattern for rotation invariant texture classification.

Taha H Rassem,Bee Ee Khoo

doi:10.1155/2014/373254

Abstract

Despite the fact that the two texture descriptors, the completed modeling of Local Binary Pattern (CLBP) and the Completed Local Binary Count (CLBC), have achieved a remarkable accuracy for invariant rotation texture classification, they inherit some Local Binary Pattern (LBP) drawbacks. The LBP is sensitive to noise, and different patterns of LBP may be classified into the same class that reduces its discriminating property. Although, the Local Ternary Pattern (LTP) is proposed to be more robust to noise than LBP, however, the latter's weakness may appear with the LTP as well as with LBP. In this paper, a novel completed modeling of the Local Ternary Pattern (LTP) operator is proposed to overcome both LBP drawbacks, and an associated completed Local Ternary Pattern (CLTP) scheme is developed for rotation invariant texture classification. The experimental results using four different texture databases show that the proposed CLTP achieved an impressive classification accuracy as compared to the CLBP and CLBC descriptors.

Highlights

Nowadays, texture analysis and classification have become one of the important areas of computer vision and image processing
The Outex datasets include 16 test suites starting from Outex TC 00010 (TC10) to Outex TC 00016 (TC16) [31]
For TC10, 480 images are used as training data

Summary

Introduction

Texture analysis and classification have become one of the important areas of computer vision and image processing. Many textures feature extraction algorithms that have been proposed to achieve a good texture classification Most of these algorithms are focusing on how to extract distinctive texture features that are robust to noise, rotation, and illumination variance. The third category is the structural methods such as topological texture descriptors [18], invariant histogram [19], and morphological decomposition [20]. All of these algorithms as well as many other algorithms are reviewed briefly in many review papers [10, 21, 22]

Results

Discussion

Conclusion