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Tile selection method based on error minimization for photomosaic image creation

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Abstract

Photomosaic images are composite images composed of many small images called tiles. In its overall visual effect, a photomosaic image is similar to the target image, and photomosaics are also called “montage art”. Noisy blocks and the loss of local information are the major obstacles in most methods or programs that create photomosaic images. To solve these problems and generate a photomosaic image in this study, we propose a tile selection method based on error minimization. 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. Based on the principles of montage art, the quality of the generated photomosaic image can be evaluated by both global and local error. 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. Moreover, the weight coefficient of the image superposition can be used to adjust the ratio of the global and local errors. Finally, to verify the proposed method, we built a new photomosaic creation dataset during this study. 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.

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Acknowledgements

The authors would like to thank the anonymous reviewers for their valuable and insightful comments on an earlier version of this manuscript. This work was supported by the National Natural Science Foundation of China (Grant Nos. 61871196, 61673186, and 61602190), the Natural Science Foundation of Fujian Province of China (2019J01082 and 2017J01110) and the Promotion Program for Young and Middle-aged Teacher in Science and Technology Research of Huaqiao University (ZQN-YX601 and ZQN-710).

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Authors and Affiliations

  1. Department of Computer Science and Technology, Huaqiao University, Xiamen, 361021, China

    Hongbo Zhang, Xin Gao, Jixiang Du, Qing Lei & Lijie Yang

  2. Fujian Key Laboratory of Big Data Intelligence and Security, Huaqiao University, Xiamen, 361021, China

    Hongbo Zhang, Jixiang Du, Qing Lei & Lijie Yang

  3. Xiamen Key Laboratory of Computer Vision and Pattern Recognition, Huaqiao University, Xiamen, 361021, China

    Hongbo Zhang, Jixiang Du, Qing Lei & Lijie Yang

  4. School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, 518055, China

    Xin Gao

Authors
  1. Hongbo Zhang
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  2. Xin Gao
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  3. Jixiang Du
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  4. Qing Lei
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  5. Lijie Yang
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Corresponding author

Correspondence to Hongbo Zhang.

Additional information

Hongbo Zhang received a PhD in Computer Science from Xiamen University, China in 2013. Currently, he is an associate professor with the School of Computer Science and Technology of Huaqiao University, China. He is the member of Fujian key laboratory of big data intelligence and security. His research interests include computer vision and pattern recognition.

Xin Gao is currently working toward an MS degree at the Harbin Institute of Technology Shenzhen Graduate School, China. Her research interests include image processing, multimedia data analysis, computer vision and machine learning.

Jixiang Du received a PhD in Pattern Recognition and Intelligent System from the University of Science and Technology of China (USTC), China in 2005. He is currently a professor at the School of Computer Science and Technology at Huaqiao University, China. He is the director of Fujian key laboratory of big data intelligence and security. His current research interests mainly include pattern recognition and machine learning.

Qing Lei received a PhD from the Cognitive Science Department of Xiamen University, China. She joined the faculty of Huaqiao University, China in 2005. Her research interests include human motion analysis and object detection/recognition.

Lijie Yang received a PhD in software engineering from the University of Macau, China. Currently, she is an associate professor with the school of Computer Science and Technology, Huaqiao University, China. Her research interests include computer graphics and computer animation.

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Zhang, H., Gao, X., Du, J. et al. Tile selection method based on error minimization for photomosaic image creation. Front. Comput. Sci. 15, 153702 (2021). https://doi.org/10.1007/s11704-020-9242-6

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  • DOI: https://doi.org/10.1007/s11704-020-9242-6

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