Modeling Emotional Evaluation of Traditional Vietnamese Aodai Clothes Based on Computer Vision and Machine Learning

Abstract

The more that human society develops, the greater the human need for well-mannered and elegant clothes, especially traditional costumes. Selecting fine clothes for a specific occasion is always an interesting individual question. Based on computer vision and machine learning, this research proposes a Kansei (emotional) evaluation for Aodai, which is traditional and well-known Vietnamese clothes for women. Features of an Aodai image are described by color coherence vectors. Self-organizing maps (SOMs) and multilayer neural networks (NNs) are used to learn the relationships between the image features and the Kansei words. Once learned, the system can recommend which Aodai is suitable for a woman through her desired feelings. She can use this recommendation when purchasing an Aodai at online stores or selecting one from her own collection for an outing. Topics for future research include investigating other image representation methods, such as combinations of color buckets in different parts of the Aodai, using more detailed descriptions in decorative patterns, and integrating conspicuity factors such as color harmony, discriminability and visibility.