Abstract
Currently, the classification of flower species has become a hot topic in the field of image classification. Flower classification belongs to the category of fine image classification, and such images are usually represented by multiple visual features. At present, all the flower classification methods based on a single convolutional neural network (CNN) model can hardly extract the features of a flower image as much as possible. In view of the limitation of description methods for flower features and the problem of low accuracy of flower species recognition, this paper proposes a flower classification framework based on ensemble of CNNs. The method consists of the following three parts: (1) The same flower image is processed differently to make the color, texture and gradient of the flower image more prominent; (2) Fine-tune the structure and parameters of the convolutional neural network to adapt it to the extraction of corresponding features. Then use the CNN model with different characteristics to extract the corresponding features; and (3) A framework that can fuse each CNN sub-learner is used to combine various features effectively. We tested the effectiveness of our method on the Oxford Flowers 102 Dataset [2]. The result demonstrates that the proposed approach effectively improves the accuracy of flower classification.
Keywords
- Flower classification
- Multi-feature
- Ensemble learning
- Convolutional neural network
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Nilsback, M.E., Zisserman, A.: Automated flower classification over a large number of classes. In: Computer Vision, Graphics & Image Processing, pp. 722–729 (2009)
Nilsback, M.E., Zisserman, A.: An automatic visual flora-segmentation and classification of flower images. Ph.D. thesis, University of Oxford (2009)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: International Conference on Neural Information Processing System, pp. 1097–1105 (2012)
Angelova, A., Zhu, S.: Efficient object detection and segmentation for fine-grained recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 811–818 (2013)
Murray, N., Perronnin, F.: Generalized max pooling. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2473–2480 (2014)
Nilsback, M.E., Zisserman, A.: Delving into the Whorl of flower segmentation. In: Proceedings of the British Machine Vision Conference, pp. 1049–1062 (2007)
Chai, Y., Lempitsky, V., Zisserman, A.: Bicos: a bi-level co-segmentation method for image classification. In: IEEE International Conference on Computer Vision, pp. 2579–2586 (2012)
Ito, S., Kubota, S.: Object classification using heterogeneous co-occurrence features. In: European Conference on Computer Vision, pp. 701–714 (2010)
Liu, Y., Tang, F., Zhou, D., Meng, Y., Dong, W.: Flower classification via convolutional neural network. In: IEEE International Conference on Functional-Structural Plant Growth Modeling, pp. 110–116(2017)
Yu, K., Jia, L., Chen, Y., Xu, W.: Deep learning: yesterday, today, and tomorrow. J. Comput. Res. Dev. 20(6), 1349 (2013)
Wu, X., Gao, L., Yan, M., Zhao, F.: Flower species recognition based on fusion of multiple features. J. Beijing For. Univ. 39(4), 86–93 (2017)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 91–110 (2004)
Varma, M., Ray, D.: Learning the discriminative power-invariance trade-off. In: IEEE International Conference on Computer Vision, pp. 1–8 (2007)
Saitoh, T., Kaneko, T.: Automatic recognition of wild flowers. J. Syst. Comput. Jpn. 34(10), 90–101 (2000)
Mishra, P.K., Maurya, S.K., Singh, R.K., Misra, A.K.: A semi automatic plant identification based on digital leaf and flower images. In: International Conference on Advances in Engineering, pp. 68–73 (2012)
Liu, Y., Yao, X.: Ensemble learning via negative correlation. J. Neural Netw. 12(10), 1399–1404 (1999)
Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On combining classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 20(3), 226–239 (1998)
Kuncheva, L.I.: Combining pattern classifiers: methods and algorithms. IEEE Trans. Neural Netw. 18, 964 (2007)
Qi, G., Hua, X., Zhang, H.: Learning semantic distance from community-tagged media collection. In: International ACM Conference on Multimedia, pp. 243–252 (2009)
Qi, G., Aggarwal, C., Huang, T.: Link prediction across networks by biased cross-network sampling. In: IEEE International Conference on Data Engineering, pp. 793–804 (2013)
Yan, C., Li, L., Zhang, C., Liu, B., Zhang, Y., Dai, Q.: An effective Uyghur text detector for complex background images. IEEE Trans. Multimed. 99, 1 (2018)
Kociołek, M., Materka, A., Strzelecki, M., Szczypiński, P.: Discrete wavelet transform—derived features for digital image texture analysis. In: International Conference on Signals and Electronic Systems, pp. 514–524 (2001)
Furht, B.: Discrete wavelet transform (DWT). In: Furht, B. (ed.) Encyclopedia of Multimedia. Springer, Boston (2006). https://doi.org/10.1007/0-387-30038-4
Guru, D.S., Kumar, Y.H.S., Manjunath, S.: Textural features in flower classification. Math. Comput. Model. 54(3–4), 1030–1036 (2011)
Seeland, M., Rzanny, M., Alaqraa, N., Wäldchen, J., Mäder, P.: Plant species classification using flower images—a comparative study of local feature representations. Plos One 12(2), e0170629 (2017)
Nilsback, M.E., Zisserman, A.: A visual vocabulary for flower classification. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1447–1454 (2006)
Nocak, C.L., Shafer, S.A.: Color edge detection. In: Proceedings DARPA Image Understanding Workshop, pp. 35–37(1987)
Dutta, D., Chaudhuri, B.B.: A color edge detection algorithm in RGB color space. In: International Conference on Advances in Recent Technologies in Communication and Computing, pp. 337–340(2009)
Gehler, P., Nowozin, S.: On feature combination for multiclass object classification. IEEE International Conference on Computer Vision, pp. 221–228 (2010)
He, N., Cao, J., Song, L.: Scale space histogram of oriented gradients for human detection. Int. Symp. Inf. Sci. Eng. 2, 167–170 (2008)
Bosch, A., Zisserman, A., Munoz, X.: ACM International Conference on Image and Video Retrieval, pp. 401–408 (2007)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2169–2178 (2006)
Wang, P., Li, L., Yan, C.: Image classification by principal component analysis of multi-channel deep feature. In: IEEE Global Conference on Signal and Information Processing, pp. 696–700 (2017)
Yan, C., Xie, H., Yang, D., Yin, J., Zhang, Y., Dai, Q.: Supervised hash coding with deep neural network for environment perception of intelligent vehicles. IEEE Trans. Intell. Transp. Syst. 19, 284–295 (2017)
Yan, C., Xie, H., Liu, S., Yin, J., Zhang, Y., Dai, Q.: Effective Uyghur language text detection in complex background images for traffic prompt identification. IEEE Trans. Intell. Transp. Syst. 19, 220–229 (2017)
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Huang, B., Hu, Y., Sun, Y., Hao, X., Yan, C. (2018). A Flower Classification Framework Based on Ensemble of CNNs. In: Hong, R., Cheng, WH., Yamasaki, T., Wang, M., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2018. PCM 2018. Lecture Notes in Computer Science(), vol 11166. Springer, Cham. https://doi.org/10.1007/978-3-030-00764-5_22
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DOI: https://doi.org/10.1007/978-3-030-00764-5_22
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