상세정보
학술지
균형 잡힌 데이터 증강 기반 영상 감정 분류에 관한 연구
- 발행일
- 202107
- 출처
- 멀티미디어학회논문지, v.24 no.7, pp.880-889
- ISSN
- 1229-7771
- 출판사
- 한국멀티미디어학회
- 협약과제
- 21ZS1200, 인간중심의 자율지능시스템 원천기술연구, 최정단
- 초록
- 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 제안 키워드
- Computer Vision(CV), Conventional methods, Data Augmentation, Image classification, Public Datasets, class imbalance, classification accuracy, everyday life, image transformations, imbalanced dataset, loss function