An efficient multi-scale overlapped block LBP approach for leaf image recognition

Authors:
Xiao-Ming Ren;Xiao-Feng Wang;Yang Zhao
Affiliations:
Department of Automation, University of Science and Technology of China, Hefei, Anhui, China,Intelligent Computing Laboratory, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, ...;Intelligent Computing Laboratory, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui, China,Key Lab of Network and Intelligent Information Processing, Hefei Univers ...;Department of Automation, University of Science and Technology of China, Hefei, Anhui, China,Intelligent Computing Laboratory, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, ...
Venue:
ICIC'12 Proceedings of the 8th international conference on Intelligent Computing Theories and Applications
Year:
2012

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Abstract

In this paper, an effective method based on multi-scale overlapped block LBP is proposed for plant leaf image recognition. Firstly, multi-scale pyramid is employed in order to improve the leaf data utilization. For each scale, each training image is divided into several equal overlapping blocks to extract the LBP histograms. Then, the PCA method is used for LBP feature dimension reduction. Finally, the recognition experiments are performed by using the SVM classifier. We compare the proposed method with Histogram of Oriented Gradients (HOG) method and Inner-Distance Shape Context (IDSC) method on Swedish leaf dataset and our ICL leaf dataset. The experimental results show that the proposed method achieves better performance than IDSC and HOG.