Insights into few shot learning approaches for image scene classification

Image understanding and scene classification are keystone tasks in computer vision. The development of technologies and profusion of existing datasets open a wide room for improvement in the image classification and recognition research area. Notwithstanding the optimal performance of exiting machin...

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Bibliographic Details
Published in:PeerJ. Computer science 2021-09, Vol.7, p.e666-e666, Article e666
Main Authors: Soudy, Mohamed, Afify, Yasmine, Badr, Nagwa
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial Intelligence
Classification
Computer Vision
Data Mining and Machine Learning
Datasets
Few shot learning
Image classification
Machine learning
Machine vision
Object recognition
Places
Reptile
Researchers
Scene classification
Software
Sun397
Training
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Summary:Image understanding and scene classification are keystone tasks in computer vision. The development of technologies and profusion of existing datasets open a wide room for improvement in the image classification and recognition research area. Notwithstanding the optimal performance of exiting machine learning models in image understanding and scene classification, there are still obstacles to overcome. All models are data-dependent that can only classify samples close to the training set. Moreover, these models require large data for training and learning. The first problem is solved by few-shot learning, which achieves optimal performance in object detection and classification but with a lack of eligible attention in the scene classification task. Motivated by these findings, in this paper, we introduce two models for few-shot learning in scene classification. In order to trace the behavior of those models, we also introduce two datasets (MiniSun; MiniPlaces) for image scene classification. Experimental results show that the proposed models outperform the benchmark approaches in respect of classification accuracy.
ISSN:2376-5992
2376-5992
DOI:10.7717/peerj-cs.666