Fuzzy region competition-based auto-color-theme design for textile images

Abstract

We propose a model to recolor textile images by different color themes. The model contains three phases. The first phase is to partition an input textile image into several homogeneous regions. The CIELab color mean of each region and a bias-field function are obtained from the segmentation results. The combination of the color mean values of all regions is considered as the color theme of the input image. The second phase is to retrieve the relevant color themes from a given dataset. The retrieved color themes preserve the color mood of the input image in the sense of the similarity measurement defined in the color mood space. In the third phase, we reconstruct new images with different appearances from the input image by using the retrieved color themes. The proposed method provides a powerful tool for designers to generate and search for all relevant color combinations related to a given theme. Numerical results indicate that our recolorization model performs well on complex textile design patterns.