Car Emotion Labeling Based on Color-SSL Semi-Supervised Learning Algorithm by Color Augmentation

In the era of emotional consumption, it has become a hot topic that commodities meet consumers’ emotional needs. As a necessity of life, the car also needs to meet the needs of consumers. To achieve that consumers can purchase cars according to their emotional needs, we need to label cars with emoti...

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Veröffentlicht in:International journal of intelligent systems Jg. 2023; H. 1
Hauptverfasser: Guo, Zhuen, Lin, Li, Liu, Baoqi, Zhang, Li, Chang, Kaixin, Li, Lingyun, Jiang, Zuoya, Wu, Jinmeng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Hindawi 2023
John Wiley & Sons, Inc
Schlagworte:
Accuracy
Algorithms
Attitudes
Automobiles
Color
Consumers
Data augmentation
Datasets
Design
Efficiency
Emotional factors
Emotions
Energy consumption
Globalization
Intelligent systems
Labeling
Machine learning
Product differentiation
Semi-supervised learning
Training
ISSN:0884-8173, 1098-111X
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Zusammenfassung:In the era of emotional consumption, it has become a hot topic that commodities meet consumers’ emotional needs. As a necessity of life, the car also needs to meet the needs of consumers. To achieve that consumers can purchase cars according to their emotional needs, we need to label cars with emotional words. The car’s appearance is the crucial medium of emotional information transmission, especially the car’s color is an essential emotional factor. As the first impression of products, color affects people’s emotional attitude. Therefore, introducing color features into the training process of sample marking is an excellent idea for intelligent labeling of a large number of product emotions. This paper proposes a semi-supervised learning method, Color-SSL, based on color data augmentation to realize the label of car emotion. Color-SSL takes FlexMatch as the framework of a semi-supervised learning model and augments data by extracting subject color. Compared with the baseline method, the accuracy of this method improved by 3.2%, 8.3%, 8.6%, and 1.4% with 10, 50, 100, and 200 training samples and 1000 test samples. The results show that Color-SSL obtains the best emotion-label result (94%). In addition, this study publishes pictures of emotional car datasets with high resolution, orthogonal perspective, and uniform background.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0884-8173
1098-111X
DOI:10.1155/2023/4331838