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

机译:基于均值漂移聚类的中国传统服饰主色智能识别

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摘要

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.
机译:中国传统服饰已被公认为西方设计师获得东方灵感和创造亚洲别致风格的最有影响力的来源之一,尤其是主导色彩和设计风格。本文提出了一种基于均值漂移聚类算法的有效颜色聚类方法。该方法包括四个步骤:(1)服装图像获取;(2)服装图像去噪;(3)对象分割;(4)颜色聚类和主色提取。首先,使用数码SLR(单镜头反光)相机拍摄服装图像。其次,三个颜色通道中的子图像分别通过中值滤波器进行滤波。第三,在Lab颜色空间中基于背景颜色对滤波后的图像进行分割,并将对象服装与背景分离。第四,通过均值漂移聚类算法将服装图像的像素分为几个聚类,并从分类结果中提取出主色。实验结果表明,当均值漂移聚类算法的带宽设置为0.05时,该方法可以从服装图像中提取出主色调。

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