Development of a machine learning model for extracting image prominent colors

Abstract

Although an image can consist of myriad different colors, due to the limitations of the human visual system and the mechanism of selective visual attention, only a limited number of them are prominent, that is, they stand out or are noticeable at first sight. In this article, a framework for building a model for extracting image prominent colors based on machine learning is presented. The model is learned on human‐extracted themes of prominent colors and uses numerous features, which were defined based on the properties of human visual system. For the purpose of this study, we constructed a database of images with their associated human‐extracted themes of prominent colors, which are open to the public and available to other researchers. The analysis of observers' data shows a high interobserver agreement on prominent color categories as well as high diversity of prominent colors. According to our results, the most influential factors on the perception of prominent colors are associated with color coverage (which should be adjusted with a saliency map), color properties—lightness and chroma, and diversity of colors. To the best of our knowledge, this is the first study on image prominent colors and first attempt to extract them with a model trained on the real data. The presented model has a high practical importance, since it can be used for extracting image colors in different scenarios, for example, for automatic color design, image categorization, as a descriptor in content‐based image retrieval, and image content analysis frameworks.

Automated Image-Based User Interface Color Theme Generation

“…Several methods for extracting colors from images have been proposed in the past. Based on their working principle, color extraction methods can be divided into five main categories: histogram-based [33,34], cluster-based [35][36][37], segmentation-based [38,39], data-driven [40,41], and hierarchical methods [42,43]. Recently, interest in methods that mimic human perception has increased considerably.…”

“…Recently, interest in methods that mimic human perception has increased considerably. Such methods have a high practical value as they are generally better accepted by users [40,[44][45][46].…”

“…Although the number of initial colors is not mandatory, we recommend at least three for optimal results. In this study, the initial colors were determined using a model for extracting the most prominent colors from an image [40]. We chose this model to ensure that the initial colors match the feature image.…”

“…The coverage of color in the image was determined by calculating the soft recoloring error. A detailed explanation of this metric can be found in our earlier work [40].…”

Interactive genetic color matching design of cultural and creative products considering color image and visual aesthetics

“… Su and Sun (2019) developed a novel framework for the color transfer between color images, which can further achieve emotion transfer between color images based on the human emotion (human feeling) or a predefined color-emotion model. Weingerl et al. (2020) presented a framework for building a model for extracting prominent colors from the image based on machine learning.…”