Abstract
Our group is studying tree species recognition using image processing technology. In the previous research, we proposed an image-based bark recognition using CNN. In this paper, we propose a method of recognizing bark image using Vision Transformer (ViT), which has attracted attention in the image recognition task in recent years. Four public datasets of NewBarkTex, TRUNK12, BarkNet1.0, and Bark-101, and a new dataset of 150 tree species originally collected, KyutechBark150, were used in the evaluation experiment. Several CNN models were used as comparison methods. As a result of the recognition experiment, the highest recognition accuracy of ViT was obtained in all the datasets. In addition, the trained model was visualized by t-SNE and attention map, and this paper shows that ViT is effective for bark image recognition.
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References
Boudra, S., Yahiaoui, I., Behloul, A.: A set of statistical radial binary patterns for tree species identification based on bark images. Multim. Tools Appl. 80, 22373–22404 (2021). https://doi.org/10.1007/s11042-020-08874-x
Carpentier, M., Giguere, P., Gaudreault, J.: Tree species identification from bark images using convolutional neural networks. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1075–1081 (2018). https://doi.org/10.1109/IROS.2018.8593514
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with Atrous separable convolution for semantic image segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 833–851. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_49
Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. In: International Conference on Learning Representations (ICLR) (2021)
Fiel, S., Sablatnig, R.: Automated identification of tree species from images of the bark, leaves and needles. In: 16th Computer Vision Winter Workshop (2011)
Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981). https://doi.org/10.1145/358669.358692
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016).. https://doi.org/10.1109/CVPR.2016.90
Ido, J., Saitoh, T.: CNN-based tree species identification from bark image. In: 10th International Conference on Graphics and Image Processing (ICGIP 2018). vol. 11069 (2019). https://doi.org/10.1117/12.2524213
Ido, J., Saitoh, T.: Automatic tree species identification from natural bark image. In: 11th International Conference on Graphics and Image Processing (ICGIP 2019). vol. 11373, pp. 29–34 (2020). https://doi.org/10.1117/12.2557187
Kingma, D., Ba, J.: Adam: a method for stochastic optimization. arXiv:1412.6980 (2014). 10.48550/arXiv. 1412.6980
van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)
Nanni, L., Paci, M., Brahnam, S., Lumini, A.: Comparison of different image data augmentation approaches. J. Imaging 7(12) (2021). https://doi.org/10.3390/jimaging7120254
Porebski, A., Vandenbroucke, N., Macaire, L., Hamad, D.: A new benchmark image test suite for evaluating colour texture classification schemes. Multim. Tools Appl. 70, 543–556 (2014). https://doi.org/10.1007/s11042-013-1418-8
Ratajczak, R., Bertrand, S., Crispim-Junior, C., Tougne, L.: Efficient bark recognition in the wild. In: International Conference on Computer Vision Theory and Applications (VISAPP2019) (2019)
Remes, V., Haindl, M.: Bark recognition using novel rotationally invariant multispectral textural features. Pattern Recogn. Lett. 125, 612–617 (2019). https://doi.org/10.1016/j.patrec.2019.06.027
Saitoh, T., Iwata, T., Wakisaka, K.: Okiraku search: Leaf images based visual tree search system. In: 14th IAPR International Conference on Machine Vision Applications (MVA), pp. 242–245 (2015). https://doi.org/10.1109/MVA.2015.7153176
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014). 10.48550/arXiv. 1409.1556
Švab, M.: Computer-vision-based tree trunk recognition. B.sc thesis, Fakulteta za računalništvo in informatiko, Univerza v Ljubljani (2014)
Wu, F., Gazo, R., Benes, B., Havia, E.: Deep barkid: a portable tree bark identification system by knowledge distillation. Eur. J. Forest Res. 140, 1391–1399 (2021). https://doi.org/10.1007/s10342-021-01407-7
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Yamabe, T., Saitoh, T. (2023). Vision Transformer-Based Bark Image Recognition for Tree Identification. In: Yan, W.Q., Nguyen, M., Stommel, M. (eds) Image and Vision Computing. IVCNZ 2022. Lecture Notes in Computer Science, vol 13836. Springer, Cham. https://doi.org/10.1007/978-3-031-25825-1_37
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