An Efficient Multi-scale Overlapped Block LBP Approach for Leaf Image Recognition
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.
KeywordsLeaf recognition Local Binary Pattern Multi-Scale pyramid Principal Component Analysis
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