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The experimental results show that the proposed method achieves a low mean absolute error and that the generated photomosaic images have a more artistic effect than do the existing approaches.- Up to 2 million images in the image collection. Finally, to verify the proposed method, we built a new photomosaic creation dataset during this study. Moreover, the weight coefficient of the image superposition can be used to adjust the ratio of the global and local errors. Under the proposed framework, via an error function analysis, the results show that selecting a tile image using a global minimum distance minimizes both the global error and the local error simultaneously. Based on the principles of montage art, the quality of the generated photomosaic image can be evaluated by both global and local error. A photomosaic image can be generated by partitioning the target image in a rectangular pattern, selecting appropriate tile images, and then adding them with a weight coefficient. To solve these problems and generate a photomosaic image in this study, we propose a tile selection method based on error minimization. Noisy blocks and the loss of local information are the major obstacles in most methods or programs that create photomosaic images. In its overall visual effect, a photomosaic image is similar to the target image, and photomosaics are also called “montage art”. Photomosaic images are composite images composed of many small images called tiles. The results indicate that our algorithm performs the best. In our experiment, the proposed algorithm is compared with the state of the art algorithms and software. We give prior knowledge to the optimization algorithm which makes the optimization process converges faster. In this paper, we present an algorithm called clustering based evolutionary programming to deal with the problem. However, little work has been done on applying evolutionary algorithms to photomosaic problem. Evolutionary algorithms imitating the process of natural selection are popular and powerful in combinatorial optimization problems. With the constraint, the photomosaic problem becomes a combinatorial optimization problem. To obtain a better visual sense and satisfy some commercial requirements, a constraint that a tiny image should not be repeatedly used many times is usually added. Most of these algorithms are designed with greedy algorithms to match the blocks with the tiny images.
In the past, many algorithms have been proposed trying to automatically compose photomosaic images. Small tiny images which replace blocks of the original image can be seen clearly by viewing it from a short distance.
A complete form can be seen clearly by viewing it from a long distance. Photomosaic images are a type of images consisting of various tiny images. Extensive experimental and user study results show that this method can generate visual pleasing collages.
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Finally, using a semantic-color metric, map the picture set to the patch set to generate the final collage. Third, construct a Gestalt-based energy function to choose the most visually pleasing and orderly patch set as the final layout.
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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. 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.
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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. In this paper, we present a visual perception driven method for automatically synthesizing visually pleasing collages.
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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.
A collage is a composite artwork made from the spatial layout of multiple pictures on a canvas, collected from the Internet or user photographs.
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