Abstract
This paper presents a new approach for leaf image set classification, where each training and testing set contains many image instances of a leaf. This approach efficiently extends binary classifiers for the task of multi-class image set classification. First, the training set is divided into two part using clustering algorithms: one will train a classifier with the images of the query set; the rest of the training set will evaluate the trained classifier and then predict the class of the query image set. The PHOG feature and Gist feature of leaf image set are merged into the whole feature of leaf image sets. Extensive experiments and comparisons with existing methods show that the proposed approach achieves state of the art performance for leaf image set recognition.
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Acknowledgement
This work was supported by the Grant of the National Science Foundation of China (No. 61175121), the Program for New Century Excellent Talents in University (No. NCET-10-0117), the Grant of the National Science Foundation of Fujian Province (No. 2013J06014), the Promotion Program for Young and Middle-aged Teacher in Science and Technology Research of Huaqiao University (No. ZQN-YX108).
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Zhang, YH., Du, JX., Wang, J., Zhai, CM. (2015). Reverse Training for Leaf Image Set Classification. In: Huang, DS., Han, K. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2015. Lecture Notes in Computer Science(), vol 9227. Springer, Cham. https://doi.org/10.1007/978-3-319-22053-6_25
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DOI: https://doi.org/10.1007/978-3-319-22053-6_25
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