An Optimized CLBP Descriptor Based on a Scalable Block Size for Texture Classification

• Jianjun Li,
• Susu Fan,
• Zhihui Wang,
• Haojie Li,
• Chin-Chen Chang,

Vol. 11, No. 1, January 29, 2017
10.3837/tiis.2017.01.015, Download Paper (Free):

• Texture classification and recognition
• LBP
• CLBP
• SVM
• Scalable block size

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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