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.

Color plays an essential role in the design of user interfaces and significantly impacts the user experience. To create aesthetically pleasing and user-friendly interfaces, the colors of the user interface should be consistent with the images. The latter can be challenging to achieve, as images often have different colors and are often changed by editors or authors who do not have sufficient design knowledge. To solve this problem, we have developed a model that automatically determines the color theme of the user interface based on a given image. The model first extracts the most prominent colors from the image and then considers both aesthetic (color harmony and compatibility with the image) and usability aspects (color contrast, color diversity, and color strength). All color calculations are performed in the perceptually uniform color space CAM02-UCS. In addition, the model can be adapted to the user’s needs and requirements. To test the model, we implemented it in a web-based application in which the colors were automatically selected based on the featured image. The resulting color themes were then evaluated by the users, who were mainly professional designers. According to the results, the model generates color themes that are consistent with the image, aesthetic, and user-friendly. An important observation was also that color harmony can be achieved simply by using the most prominent colors of the image (regardless of their hue), suggesting that color harmony is strongly influenced by the context of use. The presented model holds significant practical importance as it can be utilized in various applications and tools. For instance, it can automatically choose a color theme for a user interface based on a particular image, such as a company logo or a product image. Moreover, it can dynamically adjust the colors of elements in real time based on the image that is visible simultaneously with the elements.

The colour represents an essential element of visual and graphic communications. It plays an important role in the perception of visual design and it is significant for all participants in the process of planning, developing and...

The length of a word depends on the number of characters that make it up. Since we are constantly confronted with words (information), a suitable typeface should be chosen to make reading comfortable and easy. The number of characters can affect how visible a word is and, more importantly, how quickly the word can be read and understood. For this reason, we conducted tests with five-letter words randomly displayed at the four positions on the screen. The study examined the minimum time required to recognize five-letter words. Five different typefaces (Calibri, Georgia, Swiss 721, Trebuchet, Verdana) were included in the study to determine which of the screen typefaces read the fastest. The Georgia typeface performed the best regardless of the other conditions. The time to read upper-case letters was much shorter than lower-case and sentence-case letters. For words presented in the upper positions of the screen, the recognition time was shorter than for the lower positions of the screen. Different combinations of variables showed that some were better suited for on-screen use.

V sestavku je predstavljen del procesa izdelave interaktivnega Tesnièrjevega Lingvističnega atlasa za študij dvojine v slovenščini. Predstavljeno delo se osredotoča na razvoj uporabniškega vmesnika in zajema naslednje korake: analizo podobnih obstoječih interaktivnih atlasov, raziskavo uporabnikov, načrtovanje uporabniške izkušnje, oblikovanje vmesnika in izdelavo prototipa. Rezultat predstavljenega dela je delujoč prototip, ki bo služil kot osnova za njegov končni razvoj interaktivnega Tesnièrjevega lingvističnega atlasa.