Abstract
Local Binary Pattern (LBP) has been widely used in texture classification because of its simplicity and computational efficiency. Traditional LBP codes the sign of the local difference and uses the histogram of the binary code to model the given image. However, the directional statistical information is ignored in LBP. In this paper, some directional statistical features, specifically the mean and standard deviation of the local absolute difference are extracted and used to improve the LBP classification efficiency. In addition, the least square estimation is used to adaptively minimize the local difference for more stable directional statistical features, and we call this scheme the adaptive LBP (ALBP). By coupling the directional statistical features with ALBP, a new rotation invariant texture classification method is presented. Experiments on a large texture database show that the proposed texture feature extraction and classification scheme could significantly improve the classification accuracy of LBP.
Original language | English |
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Title of host publication | 2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings |
Pages | 285-288 |
Number of pages | 4 |
DOIs | |
Publication status | Published - 1 Dec 2010 |
Event | 2010 17th IEEE International Conference on Image Processing, ICIP 2010 - Hong Kong, Hong Kong Duration: 26 Sept 2010 → 29 Sept 2010 |
Conference
Conference | 2010 17th IEEE International Conference on Image Processing, ICIP 2010 |
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Country/Territory | Hong Kong |
City | Hong Kong |
Period | 26/09/10 → 29/09/10 |
Keywords
- LBP
- LSE
- Rotation invariance
ASJC Scopus subject areas
- Software
- Computer Vision and Pattern Recognition
- Signal Processing