Unsupervised Metric Learning for Expressing Color and Shape Information to Uncover Abstract Connections within Image Datasets

In this research, we propose a novel approach using unsupervised metric learning tailored to datasets characterized by complex similarities and connections, such as those found in paintings and makeup, which are challenging to express linguistically. These datasets often present the difficulty of adequately analyzing data points due to the intricate interplay of defining elements, a limitation of traditional labeling methods. Additionally, the high degree of specialization required makes annotation significantly costly. Unsupervised metric learning emerges as a powerful method for extracting more cost-effective features and for the comprehensive analysis of these datasets. Expanding upon previous research that utilized style transfer models, our study further explores feature design, specifically focusing on extracting detailed information about critical aspects of similarity assessment, such as color and shape. Our model adeptly incorporates visual information, unveiling the hidden abstract connections within datasets. We validated our approach using a dataset of Ukiyo-e, a genre of Japanese painting, and achieved accuracy comparable to supervised learning models. This research opens up new possibilities for the analysis of complex image datasets with abstract relational depth, fostering a deeper understanding of the data.