Abstract
The requisite for a large number of training images for each class is a burden to Deep Learning (DL) research. As a result, Few Shot Learning (FSL) techniques have been developed that require only a few number of samples of a class to classify a query sample of that class. In this paper, ResNet-50 and Prototypical Networks (ProNet)-based FSL approach is proposed to classify images. The proposed approach uses ResNet-50 Convolutional Neural Networks (CNN) model for feature extraction and the ProNet for classifying query images. The ProNet computes a mean called prototype of all the support images of a class, which is then used to classify a query image by comparing it with the Euclidean distance. The Plant Village (PV) dataset is used to analyze the results of the proposed approach. The dataset is split into a source domain for training the technique and a target domain for testing its performance. The proposed model is trained episodically on 4000 training tasks randomly generated from the images of source domain. The target domain is split into four sets, for N = 5 (5 classes randomly selected from target domain) and K = 10 (for each of the 5 classes 10 samples are selected), the approach delivered 70.24% accuracy on split-1, 89, 84.5, and 92.26% accuracies on split-2, 3, and 4, respectively. In comparison, the proposed approach delivered better result.
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References
Sarkar NK, Singh MM, Nandi U (2022) Learning based image classification techniques. In: Mukhopadhyay S, Sarkar S, Dutta P, Mandal JK, Roy S (eds) Computational intelligence in communications and business analytics. CICBA 2022. Communications in computer and information science, vol 1579. Springer, Cham. https://doi.org/10.1007/978-3-031-10766-5-3
Krizhevsky A, Sutskever I, Hinton GE (2017) Imagenet classification with deep convolutional neural networks. Commun ACM 60(6):84–90
Sarkar NK, Singh MM, Nandi U (2022) Recent researches on image classification using deep learning approach. Int J Comput Digit Syst 12(1):1357–1374
Sarkar NK, Singh MM, Nandi U (2023) A novel deep neural network model using network deconvolution with attention based activation for crop disease classification. Multimed Tools Appl. https://doi.org/10.1007/s11042-023-16125-y
Liu B et al (2018) Deep few-shot learning for hyperspectral image classification. IEEE Trans Geosci Remote Sens 57(4):2290-2304
Liu S, Shi Q, Zhang L (2020) Few-shot hyperspectral image classification with unknown classes using multitask deep learning. IEEE Trans Geosci Remote Sens 59(6):5085–5102
Li Z et al (2021) Deep cross-domain few-shot learning for hyperspectral image classification. IEEE Trans Geosci Remote Sens 60:1–18
Pal D et al (2021) SPN: Stable prototypical network for few-shot learning-based hyperspectral image classification. IEEE Geosci Remote Sens Lett 19:1–5
Abdrakhmanov R, Altynbekov M, Abu A, Shomanov A, Viderman D, Lee M-H (2021) Few-shot learning approach for COVID-19 detection from X-ray images. In: 2021 16th international conference on electronics computer and computation (ICECCO), Kaskelen, Kazakhstan, pp 1–3. https://doi.org/10.1109/ICECCO53203.2021.9663860.
Chen D, Chen X, Lu P, Wang X, Lan X (2023) CNFRD: a few-shot rumor detection framework via capsule network for COVID-19. Int J Intell Syst 2023:2467539. https://doi.org/10.1155/2023/2467539
Li Y, Chao X (2021) Semi-supervised few-shot learning approach for plant diseases recognition. Plant Methods 17:68. https://doi.org/10.1186/s13007-021-00770-1
Argüeso D et al (2020) Few-shot learning approach for plant disease classification using images taken in the field. Comput Electron Agric 175:105542
Huang H et al (2020) Low-rank pairwise alignment bilinear network for few-shot fine-grained image classification. IEEE Trans Multimedia 23:1666–1680
Nuthalapati SV, Tunga A (2021) Multi-domain few-shot learning and dataset for agricultural applications. In: Proceedings of the IEEE/CVF international conference on computer vision
Afifi A, Alhumam A, Abdelwahab A (2021) Convolutional neural network for automatic identification of plant diseases with limited data. Plants 10:28. https://doi.org/10.3390/plants10010028
Finn C, Abbeel P, Levine S (2017) Model-agnostic meta-learning for fast adaptation of deep networks. arXiv:1703.03400
Snell J, Swersky K, Zemel R (2017) Prototypical networks for few-shot learning. In: Proceedings of advances in neural information processing systems, pp 4077–4087
Pal D et al (2021) SPN: Stable prototypical network for few-shot learning-based hyperspectral image classification.IEEE Geosci Remote Sens Lett 19:1–5
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778. doi.org/10.48550/arXiv.1512.03385
Mohanty SP. Colored. https://github.com/spMohanty/PlantVillage-Dataset
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Singh, M.M., Sarkar, N.K., Nandi, U. (2024). Image Classification Using Few Shot Learning. In: Asirvatham, D., Gonzalez-Longatt, F.M., Falkowski-Gilski, P., Kanthavel, R. (eds) Evolutionary Artificial Intelligence. ICEASSM 2017. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-99-8438-1_7
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