Tile selection method based on error minimization for photomosaic image creation
Hongbo ZHANG, Xin GAO, Jixiang DU, Qing LEI, Lijie YANG
Tile selection method based on error minimization for photomosaic image creation
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 errorminimization. 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 lowmean absolute error and that the generated photomosaic images have a more artistic effect than do the existing approaches.
photomosaic image / tile image / target image / error minimization / mean absolute error
[1] |
Yang X, Mei T, Xu Y Q, Rui Y, Li S. Automatic generation of visualtextual presentation layout. ACM Transactions on Multimedia Computing, Communications, and Applications, 2016, 12(2): 1–22
CrossRef
Google scholar
|
[2] |
Gatys L A, Ecker A S, Bethge M. Image style transfer using convolutional neural networks. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2016, 2414–2423
CrossRef
Google scholar
|
[3] |
Gao P, Wu J, Lin Y, Xia Y, Mao T. Fast Chinese calligraphic character recognition with large-scale data. Multimedia Tools and Applications, 2015, 74(17): 1–18
CrossRef
Google scholar
|
[4] |
Seo S, Kang D. A photomosaic image generation method using photo annotation in a social network environment. Multimedia Tools and Applications, 2016, 75(20): 12831–12841
CrossRef
Google scholar
|
[5] |
Lee H Y. Generation of photo-mosaic images through block matching and color adjustment. International Journal of Computer and Information Engineering, 2014, 8(3): 457–460
|
[6] |
Blasi G D, Petralia M. Fast photomosaic. In: Proceedings of International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision. 2005, 1–2
|
[7] |
Deligiannis N, Comelis B, Rodrigues M R, Daubechies I. Multi-modal dictionary learning for image separation with application in art investigation. IEEE Transactions on Image Processing, 2016, 26(2): 751–764
CrossRef
Google scholar
|
[8] |
Li C L, Su Y, Wang R Z. Extended photomosaic with QR code capability. In: Proceedings of IEEE International Conference on Multimedia and Expo Workshops. 2017, 345–350
|
[9] |
Silvers R, Michael H. Photomosaics. New York: Henry Holt and Company, 1997
|
[10] |
Narasimhan H, Satheesh S. A randomized iterative improvement algorithm for photomosaic generation. In: Proceedings of World Congress on Nature and Biologically Inspired Computing. 2009, 777–781
CrossRef
Google scholar
|
[11] |
He Y, Zhou J, Yuen S Y. Composing photomosaic images using clustering based evolutionary programming. Multimedia Tools and Applications, 2019, 78(18): 25919–25936
CrossRef
Google scholar
|
[12] |
Lee H Y, Automatic photomosaic algorithm through adaptive tiling and block matching. Multimedia Tools and Applications, 2017, 76(22): 24281–24297
CrossRef
Google scholar
|
[13] |
Fujisawa M, Amano T, Taketomi T, Yamamoto G, Uranishi Y, Miyazaki J. Interactive photomosaic system using GPU. In: Proceedings of ACM International Conference on Multimedia. 2012, 1297–1298
CrossRef
Google scholar
|
[14] |
Yang Y, Ito Y, Nakano K. Photomosaic generation by rearranging subimages, with GPU acceleration. In: Proceedings of IEEE International Parallel and Distributed Processing Symposium Workshops. 2017, 942–951
CrossRef
Google scholar
|
[15] |
Chavan A S, Manjrekar A A. Data embedding technique using secret fragment visible mosaic image for covered communication. In: Proceedings of International Conference on Information Processing. 2016, 260–265
CrossRef
Google scholar
|
[16] |
William P, Lumsden J, Nabney I T. The Mosaic test: measuring the effectiveness of colour-based image retrieval. Multimedia Tools and Applications, 2013, 64(3): 695–716
CrossRef
Google scholar
|
[17] |
Chen D, Yuan L, Liao J, Yu N, Hua G. StyleBank: an explicit representation for neural image style transfer. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2017, 2770–2779
CrossRef
Google scholar
|
[18] |
Chu H K, Chang C S, Lee R R, Mitra N J. Halftone QR codes. ACM Transactions on Graphics, 2013, 32(6): 217
CrossRef
Google scholar
|
[19] |
Xu M, Su H, Li Y, Li X, Liao J, Niu J, Lv P, Zhou B. Stylized aesthetic QR code. IEEE Transactions on Multimedia, 2019, 21(8): 1960–1970
CrossRef
Google scholar
|
[20] |
Yu Z, Lu L, Yanwen Guo Y, Fan R, Liu M, Wang W. Content-aware photo collage using circle packing. IEEE Transactions on Visualization and Computer Graphics, 2014, 20(2): 182–195
CrossRef
Google scholar
|
[21] |
Liu L, Zhang H, Jing G, Guo Y, Chen Z, Wang W. Correlation-preserving photo collage. IEEE Transactions on Visualization and Computer Graphics, 2018, 24(6): 1956–1968
CrossRef
Google scholar
|
[22] |
Wang J, Quan L, Sun J, Tang X, Shum H Y. Picture collage. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2006, 347–354
|
[23] |
Liu T, Wang J, Sun J, Zheng N, Xiaoou Tang X, Shum H Y. Picture collage. IEEE Transactions on Multimedia, 2009, 11(7): 1225–1239
CrossRef
Google scholar
|
[24] |
Fang F, Yi M, Feng H, Hu S, Xiao C. Narrative collage of image collections by scene graph recombination. IEEE Transactions on Visualization and Computer Graphics, 2017, 24(9): 2559–2572
CrossRef
Google scholar
|
[25] |
Kuhn H W. The hungarian method for the assignment problem. Naval Research Logistics Quarterly, 1955, 2(1): 83–97
CrossRef
Google scholar
|
[26] |
Kumar N, Berg A C, Belhumeur P N, Nayaret S K. Attribute and simile classifiers for face verification. In: Proceedings of IEEE International Conference on Computer Vision. 2009, 365–372
CrossRef
Google scholar
|
/
〈 | 〉 |