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균형 잡힌 데이터 증강 기반 영상 감정 분류에 관한 연구
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- Issue Date
- 2021-07
- Citation
- 멀티미디어학회논문지, v.24, no.7, pp.880-889
- ISSN
- 1229-7771
- Publisher
- 한국멀티미디어학회
- Language
- Korean
- Type
- Journal Article
- Abstract
- In everyday life, recognizing people's emotions from their frames is essential and is a popular research domain in the area of computer vision. Visual emotion has a severe class imbalance in which most of the data are distributed in specific categories. The existing methods do not consider class imbalance and used accuracy as the performance metric, which is not suitable for evaluating the performance of the imbalanced dataset. Therefore, we proposed a method for recognizing visual emotion using balanced data augmentation to address the class imbalance. The proposed method generates a balanced dataset by adopting the random over-sampling and image transformation methods. Also, the proposed method uses the Focal loss as a loss function, which can mitigate the class imbalance by down weighting the well-classified samples. EfficientNet, which is the state-of-the-art method for image classification is used to recognize visual emotion. We compare the performance of the proposed method with that of conventional methods by using a public dataset. The experimental results show that the proposed method increases the F1 score by 40% compared with the method without data augmentation, mitigating class imbalance without loss of classification accuracy.
- KSP Keywords
- Computer Vision(CV), Conventional methods, Data Augmentation, Image Classification, Image transformation, Imbalanced datasets, Over-sampling, Public Datasets, class imbalance, classification accuracy, everyday life