In this paper, we present a content-aware method for generating a word painting. Word painting is a composite artwork made from the assemblage of words extracted from a given text, which carries similar semantics and visual features to a given source image. However, word painting, usually created by skilled artists, involves tedious manual processes, especially when generating streamlines and laying out text. Hence, we provide an easy method to create word paintings for users. How to design textural layout that simultaneously conveys the input image and enables easy access to the semantic theme, is the key challenge to generate a visually pleasing word painting. To address this issue, given an image and its content related text, we first decompose the input image into several regions and approximate each region with a smooth vector field. At the same time, by analyzing the input text, we extract some weighted keywords as the graphic elements. Then, to measure the likelihood of positions in the input image that attract the observers’ attention, we generate a saliency map with our trained visual attention model. Finally, jointly considering visual attention and aesthetic rules, we propose an energy-based optimization framework to arrange extracted keywords into the decomposed regions and synthesize a word painting. Experimental results and user studies show that this method is able to generate a fashionable and appealing word painting.

No abstract

A collage is a composite artwork made from the spatial layout of multiple pictures on a canvas, collected from the Internet or user photographs. Collages, usually made by skilled artists, involve a complex manual process, especially when searching for component pictures and adjusting their spatial layout to meet artistic requirements. In this paper, we present a visual perception driven method for automatically synthesizing visually pleasing collages. Unlike previous works, we focus on how to design a collage layout which not only provides easy access to the theme of the overall image, but also conforms to human visual perception. To achieve this goal, we formulate the generation of collages as a mapping problem: given a canvas image, first, compute a saliency map for it and a vector field for each sub-region of it. Second, using a divide-and-conquer strategy, generate a series of patch sets from the canvas image, where the salient map and the vector field are used to determine each patch’s size and direction respectively. Third, construct a Gestalt-based energy function to choose the most visually pleasing and orderly patch set as the final layout. Finally, using a semantic-color metric, map the picture set to the patch set to generate the final collage. Extensive experimental and user study results show that this method can generate visual pleasing collages.