Color configuration plays an important role in art, design, and communication, which can influence the user’s experiences, feelings, and psychological well-being. It is laborious to manually select a color theme from scratch for handling large batches of images. Alternatively, it can inspire designers’ creations and save their time as well by leveraging the color themes in existing art works (e.g. fabric, paintings). However, it is challenging to automatically extract perceptually plausible color themes from fabric images. This paper presents a new automatic framework for extracting color themes from fabric images. A saliency map is built to help recognize the visual attention regions of the input image. Since the saliency map separates the image into visual attention regions (foreground) and non-visual attention regions (background), we respectively compute the dominant colors of these two regions, and merge them to form the initial target color theme based on certain rules. The dominant colors are extracted, accounting for the characteristics of the fabric and hue distributions of fabric images so as to acquire visually plausible results. Our method can be used to transfer colors between two fabric images for fabric color design. We tested our method thoroughly with various fabric images (e.g. cotton, silk, and linen) with different texture patterns (e.g. plain and twill). Experiments show that our method is more efficient and can generate more visually plausible results than state-of-the-art algorithms.

Artists usually carefully select different colors in artistic work so as to convey special visual and emotional feelings. Color theme extraction techniques can help users to acquire the color styles in an image. However, current color theme extraction methods ignore the emotional factors, and they can only provide a single theme result for an image as well, neither of which meet people's favor on different colors under different mood states. This article introduces the conception of emotional color theme, introduces the color emotion theory into color theme extraction, and proposes a novel emotion color theme extraction framework. To achieve these goals, we perform the theme extraction with emotion value of each pixel instead of color value. The emotional discrepancy is proposed between the colors in the theme to evaluate a color theme quality and prove it with color theme dataset. Then a data driven approach is adopted to optimize the color theme. Different from previous optimizing strategies, we built the emotional relation between the target theme and the candidate theme. Our method can enhance the color editing results of previous methods, e.g., for color transfer our results can demonstrate hierarchical emotions from a single reference image. © 2015 Wiley Periodicals, Inc. Col Res Appl, 41, 513–522, 2016

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