TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
An Optimized CLBP Descriptor Based on a Scalable Block Size for Texture Classification |
| 영문제목(English Title) |
An Optimized CLBP Descriptor Based on a Scalable Block Size for Texture Classification |
| 저자(Author) |
Jianjun Li
Susu Fan
Zhihui Wang
Haojie Li
Chin-Chen Chang
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| 원문수록처(Citation) |
VOL 11 NO. 01 PP. 0288 ~ 0301 (2017. 01) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
In this paper, we propose an optimized algorithm for texture classification by computing a completed modeling of the local binary pattern (CLBP) instead of the traditional LBP of a scalable block size in an image. First, we show that the CLBP descriptor is a better representative than LBP by extracting more information from an image. Second, the CLBP features of scalable block size of an image has an adaptive capability in representing both gross and detailed features of an image and thus it is suitable for image texture classification. This paper successfully implements a machine learning scheme by applying the CLBP features of a scalable size to the Support Vector Machine (SVM) classifier. The proposed scheme has been evaluated on Outex and CUReT databases, and the evaluation result shows that the proposed approach achieves an improved recognition rate compared to the previous research results.
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| 키워드(Keyword) |
Texture classification and recognition
LBP
CLBP
SVM
Scalable block size
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| 파일첨부 |
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