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Texture plays an important role in cartoon illustrations to display object materials and enrich visual experiences. Unfortunately, manually designing and drawing an appropriate texture is not easy even for proficient artists, let alone novice or amateur people. While there exist tons of textures on the Internet, it is not easy to pick an appropriate one using traditional text-based search engines. Although several texture pickers have been proposed, they still require the users to browse the textures by themselves, which is still labor-intensive and time-consuming. In this article, an automatic texture recommendation system is proposed for recommending multiple textures to replace a set of user-specified regions in a cartoon illustration with visually pleasant look. Two measurements, the suitability measurement and the style-consistency measurement, are proposed to make sure that the recommended textures are suitable for cartoon illustration and at the same time mutually consistent in style. The suitability is measured based on the synthesizability, cartoonity, and region fitness of textures. The style-consistency is predicted using a learning-based solution since it is subjective to judge whether two textures are consistent in style. An optimization problem is formulated and solved via the genetic algorithm. Our method is validated on various cartoon illustrations, and convincing results are obtained. [ABSTRACT FROM AUTHOR]

Image recoloring is an emerging editing technique that can change the color style of an image by modifying pixel values without altering the original image content. With the rapid proliferation of social network and image editing techniques, recolored images (RIs) have raised new security issues in society. Existing detection methods have good performance in detecting RIs for certain categories of recoloring techniques. However, the performance on the handcrafted recoloring scenario is still poor due to the influence of human prior knowledge. To deal with this problem, we explore a solution from the perspective of chrominance texture artifacts to improve the generalization ability. The results of the analysis show that natural images (NIs) and RIs have textural disparities in different color components, especially in the chrominance components (i.e., Cb, Cr, and H). Based on such new prior knowledge of statistical discriminability, we propose a feature set to capture texture features in chrominance components for identifying RIs. Extensive experimental results show that the proposed method can accurately identify RIs with certain categories of recoloring techniques, and outperforms existing methods in the scenario of handcrafted recoloring. [ABSTRACT FROM AUTHOR]