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Nowadays leaf image classification is very useful for both botanists and ordinary users since advanced imaging devices such as smart phones make it ever easier to capture leaf images for various tasks such as retrieval and classification. Most of existing approaches mainly utilize global shape features. In this paper, we propose to improve leaf image classification by taking both global features and local features into account. As one of the most effective shape features, shape context is utilized as global feature. And SIFT (Scale Invariant Feature Transform) descriptors that have been successfully utilized for object recognition and image classification are selected as local features. Finally, weighted K-NN algorithm is utilized for classification. Experimental results on the large ICL dataset demonstrate that the proposed method outperforms the state-of-the-art.