Completed local ternary pattern for rotation invariant texture classification
Authors: Rassem, T.H. and Khoo, B.E.
Journal: The Scientific World Journal
Volume: 2014
eISSN: 1537-744X
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. © 2014 Taha H. Rassem and Bee Ee Khoo.
Source: Scopus
Completed local ternary pattern for rotation invariant texture classification.
Authors: Rassem, T.H. and Khoo, B.E.
Journal: ScientificWorldJournal
Volume: 2014
Pages: 373254
eISSN: 1537-744X
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.
Source: PubMed
Completed Local Ternary Pattern for Rotation Invariant Texture Classification
Authors: Rassem, T.H. and Khoo, B.E.
Journal: SCIENTIFIC WORLD JOURNAL
ISSN: 1537-744X
DOI: 10.1155/2014/373254
Source: Web of Science (Lite)
Completed local ternary pattern for rotation invariant texture classification.
Authors: Rassem, T.H. and Khoo, B.E.
Journal: TheScientificWorldJournal
Volume: 2014
Pages: 373254
eISSN: 1537-744X
ISSN: 2356-6140
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.
Source: Europe PubMed Central