Intelligent recognition of dominant colors for Chinese traditional costumes based on a mean shift clustering method

Abstract

Chinese traditional costumes have been recognized as one of the most influential sources for Western designers to obtain oriental inspirations and create Asian chic, especially the dominant colors and design style. In this paper, an effective color clustering method based on Mean shift clustering algorithm is developed for Chinese traditional costumes image. The proposed method consists of four steps: (1) costumes image acquisition, (2) costumes image denoising, (3) object segmentation, and (4) color clustering and dominant colors extraction. Firstly, a digital SLR (Single Lens Reflex) camera is used to capture the costumes images. Secondly, the sub-images in the three color channels are filtered by median filter separately. Thirdly, the filtered images are segmented based on the background color in the Lab color space, and the object costumes is separated from the background. Fourthly, the pixels of the costume image are classified into several clusters by Mean shift clustering algorithm, and the dominate colors are extracted from the classification results. The experimental results demonstrate that the proposed method can extract the dominant colors from costumes images with great accuracy when the bandwidth of Mean shift clustering algorithm is set as 0.05.