Leaf Recognition Based on Binary Gabor Pattern and Extreme Learning Machine
Automatic plant leaf recognition has been a hot research spot in the recent years, where encouraging improvements have been achieved in both recognition accuracy and speed. However, existing algorithms usually only extracted leaf features (such as shape or texture) or merely adopt traditional neural network algorithm to recognize leaf, which still showed limitation in recognition accuracy and speed especially when facing a large leaf database. In this paper, we present a novel method for leaf recognition by combining feature extraction and machine learning. To break the weakness exposed in the traditional algorithms, we applied binary Gabor pattern (BGP) and extreme learning machine (ELM) to recognize leaves. To accelerate the leaf recognition, we also extract BGP features from leaf images with an offline manner. Different from the traditional neural network like BP and SVM, our method based on the ELM only requires setting one parameter, and without additional fine-tuning during the leaf recognition. Our method is evaluated on several different databases with different scales. Comparisons with state-of-the-art methods were also conducted to evaluate the combination of BGP and ELM. Visual and statistical results have demonstrated its effectiveness.
KeywordsLeaf recognition Binary Gabor Pattern Extreme Learning Machine Leaf recognition processing batch
This work was supported in part by grants from the National Natural Science Foundation of China (No. 61303101), the Shenzhen Research Foundation for Basic Research, China (Nos. JCYJ20150324140036846), the ShenzhenPeacock Plan (No. KQCX20130621101205783) and the Start-up Research Fund of Shenzhen University (Nos. 2013-827-000009).
- 1.Wang, Z., Chi, Z., Feng, D.: Fuzzy integral for leaf image retrieval. Proc. Fuzzy Syst. 1, 372–377 (2002). IEEEGoogle Scholar
- 4.Mcneill, G., Vijayakumar, S.: 2D shape classification and retrieval. In: Ijcai 2005, Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, Edinburgh, Scotland, Uk, 30 July–August 2005, pp. 1483–1488 (2005)Google Scholar
- 6.Wu, H., Pu, P., He, G., Zhang, B., Zhao, F.: Fast and robust leaf recognition based on rotation invariant shape context. In: The 8th International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2013), pp. 145–154 (2014)Google Scholar
- 9.Wu, S.G., Bao, F.S., Xu, E.Y.: A leaf recognition algorithm for plant classification using probabilistic neural network. Comput. Sci. 2007, 11–16 (2007)Google Scholar
- 10.Zhang, L., Zhou, Z., Li, H.: Binary gabor pattern: an efficient and robust descriptor for texture classification. In: 2012 19th IEEE International Conference on Image Processing (ICIP). IEEE (2012)Google Scholar
- 13.Wu, Q., Zhou, C., Wang, C.: Feature extraction and automatic recognition of plant leaf using artificial neural network. In: Proceedings of Advances in Artificial Intelligence (2003)Google Scholar
- 14.ArunPriya, C., Balasaravanan, T., Thanamani, A.: An efficient leaf recognition algorithm for plant classification using support vector machine. In: Proceedings of the International Conference on Pattern Recognition. Informatics and Medical Engineering, pp. 428–432 (2012)Google Scholar
- 15.Song, M.: Combination of local descriptors and global features for leaf recognition. Sig. Image Process. 2(3), 23 (2011)Google Scholar
- 16.Zulkifli, Z., Saad., P., Mohtar, I.A.: Plant leaf identification using moment invariants & general regression neural network. In: 2011 11th International Conference on Hybrid Intelligent Systems (HIS), pp. 430–435. IEEE (2011)Google Scholar
- 17.Zhang, L., Zhang, D., Guo, Z.: MONOGENIC-LBP: a new approach for rotation invariant texture classification 2010, pp. 2677–2680 (2010)Google Scholar
- 19.Chang, C.-C., Lin, C.-J., et.al.: LIBSVM: a library for support vector machines. Department of Computer Science. National Taiwan University, Taipei, Taiwan (2001)Google Scholar