Abstract
The orchids families are large, diverse flowering plants in the tropical areas. It is a challenging task to classify orchid species from images. In this paper, we proposed an adaptive classification model of the orchid images by using a Deep Convolutional Neural Network (D-CNN). The first part of the model improved the quality of input feature maps using an adaptive Spatial Transformer Network (STN) module by performing a spatial transformation to warp an input image which was split into different locations and scales. We applied D-CNN to extract the image features from the previous step and warp into four branches. Then, we concatenated the feature channels and reduced the dimension by an estimation block. Finally, the feature maps would be forwarded to the prediction network layers to predict the orchid species. We verified the efficiency of the proposed method by conducting experiments on our data set of 52 classes of orchid flowers, containing 3,559 samples. Our results achieved an average of 93.32% classification accuracy, which is higher than the existing D-CNN models.
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References
Abadi, M., et al.: TensorFlow: a system for large-scale machine learning. In: OSDI, vol. 16, pp. 265–283 (2016)
Arwatchananukul, S., Charoenkwan, P., Xu, D.: POC: paphiopedilum orchid classifier. In: 2015 IEEE 14th International Conference on ICCI*CC, pp. 206–212. IEEE (2015)
Erhan, D., Szegedy, C., Toshev, A., Anguelov, D.: Scalable object detection using deep neural networks. In: Proceedings of the IEEE Conference on CVPR, pp. 2147–2154 (2014)
Guru, D., Kumar, Y.S., Manjunath, S.: Textural features in flower classification. Math. Comput. Model. 54(3–4), 1030–1036 (2011)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on CVPR, pp. 770–778 (2016)
He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 630–645. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0_38
Hiary, H., Saadeh, H., Saadeh, M., Yaqub, M.: Flower classification using deep convolutional neural networks. IET Comput. Vision 12(6), 855–862 (2018)
Jaderberg, M., Simonyan, K., Zisserman, A., et al.: Spatial transformer networks. In: Advances in Neural Information Processing Systems, pp. 2017–2025 (2015)
Johnson, J., Karpathy, A., Fei-Fei, L.: DenseCap: fully convolutional localization networks for dense captioning. In: Proceedings of the IEEE Conference on CVPR, pp. 4565–4574 (2016)
Li, L., Qiao, Y.: Flower image retrieval with category attributes. In: 2014 4th IEEE International Conference on ICIST, pp. 805–808. IEEE (2014)
Liu, W., Rao, Y., Fan, B., Song, J., Wang, Q.: Flower classification using fusion descriptor and SVM. In: 2017 International ISC2, pp. 1–4. IEEE (2017)
Liu, Y., Tang, F., Zhou, D., Meng, Y., Dong, W.: Flower classification via convolutional neural network. In: International Conference on FSPMA, pp. 110–116. IEEE (2016)
Nilsback, M.E., Zisserman, A.: A visual vocabulary for flower classification. In: 2006 IEEE Computer Society Conference on CVPR, vol. 2, pp. 1447–1454. IEEE (2006)
Nilsback, M.E., Zisserman, A.: Automated flower classification over a large number of classes. In: Sixth Indian Conference on Computer Vision, Graphics & Image Processing, 2008, ICVGIP 2008, pp. 722–729. IEEE (2008)
Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. IJCV 115(3), 211–252 (2015). https://doi.org/10.1007/s11263-015-0816-y
Sari, Y.A., Suciati, N.: Flower classification using combined a* b* color and fractal-based texture feature. Int. J. Hybrid Inf. Technol. 7(2), 357–368 (2014)
Siraj, F., Ekhsan, H.M., Zulkifli, A.N.: Flower image classification modeling using neural network. In: 2014 International Conference on IC3INA, pp. 81–86. IEEE (2014)
Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on CVPR, pp. 1–9 (2015)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on CVPR, pp. 2818–2826 (2016)
Xia, X., Xu, C., Nan, B.: Inception-v3 for flower classification. In: 2017 2nd International Conference on ICIVC, pp. 783–787. IEEE (2017)
Zawbaa, H.M., Abbass, M., Basha, S.H., Hazman, M., Hassenian, A.E.: An automatic flower classification approach using machine learning algorithms. In: 2014 International Conference on ICACCI, pp. 895–901. IEEE (2014)
Zhang, C., Liang, C., Li, L., Liu, J., Huang, Q., Tian, Q.: Fine-grained image classification via low-rank sparse coding with general and class-specific codebooks. IEEE Trans. Neural Netw. Learn. Syst. 28(7), 1550–1559 (2017)
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Sarachai, W., Bootkrajang, J., Chaijaruwanich, J., Somhom, S. (2019). Orchids Classification Using Spatial Transformer Network with Adaptive Scaling. In: Yin, H., Camacho, D., Tino, P., Tallón-Ballesteros, A., Menezes, R., Allmendinger, R. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2019. IDEAL 2019. Lecture Notes in Computer Science(), vol 11871. Springer, Cham. https://doi.org/10.1007/978-3-030-33607-3_1
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