Conceptual framework of hybrid style in fashion image datasets for machine learning

Abstract Fashion image datasets, in which each fashion has a label indicating its design attributes and styles, have contributed to the achievement of various machine learning techniques industry. Computer vision studies investigated labeling categories (such as items, colors, materials, details, styles) create datasets for supervised learning. Although considerable number been developed, different style classification criteria exist because lack understanding concerning style. Since styles reflect attributes, multiple can often be included single outfit. Thus, this study aims build Hybrid Style Framework develop dataset that efficiently applied We conducted focus group interviews with six experts determine classify hybrid images. developed 1,206,931K-fashion analyzed convergence. Finally, we model verified accuracy computer’s ability recognize Overall, concludes K-fashion are helpful, they data-driven services offer personalized solutions.