Dominant Color Detection For Online Fashion Retrievals

Authors : Sultan Zeybek, Merve Çelik

Pages : 69-80

Doi:10.55024/buyasambid.1501329

View : 75 | Download : 144

Publication Date : 2024-07-07

Article Type : Research Paper

Abstract :This paper introduces a novel approach aimed at efficiently extracting dominant colors from online fashion images. The method addresses challenges related to detecting overlapping objects and computationally expensive methods by combining K-means clustering and graph-cut techniques into a framework. This framework incorporates an adaptive weighting strategy to enhance color extraction accuracy. Additionally, it introduces a two-phase fashion apparel detection method called YOLOv4, which utilizes U-Net architecture for clothing segmentation to precisely separate clothing items from the background or other elements. Experimental results show that K-means with YOLOv4 outperforms K-means with the U-Net model. These findings suggest that the U-Net architecture and YOLOv4 models can be effective methods for complex image segmentation tasks in online fashion retrieval and image processing, particularly in the rapidly evolving e-commerce environment.
Keywords : Moda Görüntü Analizi, Baskın Renk Tespiti, K means Kümeleme, Görüntü Bölütleme