{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T16:40:43Z","timestamp":1769013643553,"version":"3.49.0"},"reference-count":39,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2021,4,27]],"date-time":"2021-04-27T00:00:00Z","timestamp":1619481600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"GRF grant from the Research Grants Council of Hong Kong","award":["11205620"],"award-info":[{"award-number":["11205620"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Graph."],"published-print":{"date-parts":[[2021,4,30]]},"abstract":"<jats:p>When adding a photo onto a graphic design, professional graphic designers often adjust its colors based on some target colors obtained from the brand or product to make the entire design more memorable to audiences and establish a consistent brand identity. However, adjusting the colors of a photo in the context of a graphic design is a difficult task, with two major challenges: (1) Locality: The color is often adjusted locally to preserve the semantics and atmosphere of the original image; and (2) Naturalness: The modified region needs to be carefully chosen and recolored to obtain a semantically valid and visually natural result. To address these challenges, we propose a learning-based approach to photo color adjustment for graphic designs, which maps an input photo along with the target colors to a recolored result. Our method decomposes the color adjustment process into two successive stages: modifiable region selection and target color propagation. The first stage aims to solve the core, challenging problem of which local image region(s) should be adjusted, which requires not only a common sense of colors appearing in our visual world but also understanding of subtle visual design heuristics. To this end, we capitalize on both natural photos and graphic designs to train a region selection network, which detects the most likely regions to be adjusted to the target colors. The second stage trains a recoloring network to naturally propagate the target colors in the detected regions. Through extensive experiments and a user study, we demonstrate the effectiveness of our selective region-based photo recoloring framework.<\/jats:p>","DOI":"10.1145\/3447647","type":"journal-article","created":{"date-parts":[[2021,4,27]],"date-time":"2021-04-27T15:20:51Z","timestamp":1619536851000},"page":"1-16","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":26,"title":["Selective Region-based Photo Color Adjustment for Graphic Designs"],"prefix":"10.1145","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4007-2776","authenticated-orcid":false,"given":"Nanxuan","family":"Zhao","sequence":"first","affiliation":[{"name":"Harvard University, USA and City University of Hong Kong, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Quanlong","family":"Zheng","sequence":"additional","affiliation":[{"name":"City University of Hong Kong, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Liao","sequence":"additional","affiliation":[{"name":"City University of Hong Kong, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Cao","sequence":"additional","affiliation":[{"name":"City University of Hong Kong, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hanspeter","family":"Pfister","sequence":"additional","affiliation":[{"name":"Harvard University, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rynson W. H.","family":"Lau","sequence":"additional","affiliation":[{"name":"City University of Hong Kong, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,4,27]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3072959.3002176"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3197517.3201275"},{"key":"e_1_2_1_3_1","volume-title":"Proc. IEEE CVPR.","author":"Arbel\u00e1ez P.","unstructured":"P. Arbel\u00e1ez , J. Pont-Tuset , J. Barron , F. Marques , and J. Malik . 2014. Multiscale combinatorial grouping . In Proc. IEEE CVPR. P. Arbel\u00e1ez, J. Pont-Tuset, J. Barron, F. Marques, and J. Malik. 2014. Multiscale combinatorial grouping. In Proc. IEEE CVPR."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2766978"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/1077399.1077408"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/1141911.1141933"},{"key":"e_1_2_1_7_1","volume-title":"Deepprop: Extracting deep features from a single image for edit propagation. In CGF","author":"Endo Yuki","year":"2016","unstructured":"Yuki Endo , Satoshi Iizuka , Yoshihiro Kanamori , and Jun Mitani . 2016 . Deepprop: Extracting deep features from a single image for edit propagation. In CGF , Vol. 35 . Wiley Online Library , 189\u2013201. Yuki Endo, Satoshi Iizuka, Yoshihiro Kanamori, and Jun Mitani. 2016. Deepprop: Extracting deep features from a single image for edit propagation. In CGF, Vol. 35. Wiley Online Library, 189\u2013201."},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.265"},{"key":"e_1_2_1_9_1","volume-title":"Proc. NIPS. 2672\u20132680","author":"Goodfellow Ian","year":"2014","unstructured":"Ian Goodfellow , Jean Pouget-Abadie , Mehdi Mirza , Bing Xu , David Warde-Farley , Sherjil Ozair , Aaron Courville , and Yoshua Bengio . 2014 . Generative adversarial nets . In Proc. NIPS. 2672\u20132680 . Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. In Proc. NIPS. 2672\u20132680."},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/2461912.2461997"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3197517.3201365"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.12498"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3072959.3073659"},{"key":"e_1_2_1_14_1","volume-title":"Proceedings of the International Conference on Machine Learning. PMLR, 448--456","author":"Ioffe Sergey","year":"2015","unstructured":"Sergey Ioffe and Christian Szegedy . 2015 . Batch normalization: Accelerating deep network training by reducing internal covariate shift . In Proceedings of the International Conference on Machine Learning. PMLR, 448--456 . Sergey Ioffe and Christian Szegedy. 2015. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In Proceedings of the International Conference on Machine Learning. PMLR, 448--456."},{"key":"e_1_2_1_15_1","volume-title":"Image-to-image translation with conditional adversarial networks. Prof","author":"Isola Phillip","year":"2017","unstructured":"Phillip Isola , Jun-Yan Zhu , Tinghui Zhou , and Alexei A Efros . 2017. Image-to-image translation with conditional adversarial networks. Prof . IEEE CVPR ( 2017 ). Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros. 2017. Image-to-image translation with conditional adversarial networks. Prof. IEEE CVPR (2017)."},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1002\/col.22144"},{"key":"e_1_2_1_17_1","volume-title":"Adam: A method for stochastic optimization. arXiv:1412.6980.","author":"Kingma Diederik P","year":"2014","unstructured":"Diederik P Kingma and Jimmy Ba . 2014 . Adam: A method for stochastic optimization. arXiv:1412.6980. Retrieved from https:\/\/arxiv.org\/abs\/1412.6980. Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv:1412.6980. Retrieved from https:\/\/arxiv.org\/abs\/1412.6980."},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/1015706.1015780"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-8659.2008.01264.x"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3072959.3073683"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00326"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.740"},{"key":"e_1_2_1_23_1","volume-title":"Proc. EG Rendering Techniques. Eurographics Association, 309\u2013320","author":"Luan Qing","year":"2007","unstructured":"Qing Luan , Fang Wen , Daniel Cohen-Or , Lin Liang , Ying-Qing Xu , and Heung-Yeung Shum . 2007 . Natural image colorization . In Proc. EG Rendering Techniques. Eurographics Association, 309\u2013320 . Qing Luan, Fang Wen, Daniel Cohen-Or, Lin Liang, Ying-Qing Xu, and Heung-Yeung Shum. 2007. Natural image colorization. In Proc. EG Rendering Techniques. Eurographics Association, 309\u2013320."},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.304"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3197517.3201393"},{"key":"e_1_2_1_26_1","unstructured":"Joseph Redmon and Ali Farhadi. 2018. Yolov3: An incremental improvement. arXiv:1804.02767. Retrieved from https:\/\/arxiv.org\/abs\/1804.02767.  Joseph Redmon and Ali Farhadi. 2018. Yolov3: An incremental improvement. arXiv:1804.02767. Retrieved from https:\/\/arxiv.org\/abs\/1804.02767."},{"key":"e_1_2_1_27_1","volume-title":"Proc. NIPS. 91\u201399","author":"Ren Shaoqing","year":"2015","unstructured":"Shaoqing Ren , Kaiming He , Ross Girshick , and Jian Sun . 2015 . Faster r-cnn: Towards real-time object detection with region proposal networks . In Proc. NIPS. 91\u201399 . Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. 2015. Faster r-cnn: Towards real-time object detection with region proposal networks. In Proc. NIPS. 91\u201399."},{"key":"e_1_2_1_28_1","volume-title":"U-net: Convolutional networks for biomedical image segmentation. In Proc","author":"Ronneberger Olaf","year":"2015","unstructured":"Olaf Ronneberger , Philipp Fischer , and Thomas Brox . 2015 . U-net: Convolutional networks for biomedical image segmentation. In Proc . MICCAI. Springer , 234\u2013241. Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015. U-net: Convolutional networks for biomedical image segmentation. In Proc. MICCAI. Springer, 234\u2013241."},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbusres.2012.05.023"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3272127.3275054"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1080\/10696679.2003.11501933"},{"key":"e_1_2_1_33_1","unstructured":"Orana Velarde. 2018. Quick Tips for Creating Social Media Graphics Like a Designer. Retrieved from https:\/\/visme.co\/blog\/add-text-to-image\/.  Orana Velarde. 2018. Quick Tips for Creating Social Media Graphics Like a Designer. Retrieved from https:\/\/visme.co\/blog\/add-text-to-image\/."},{"key":"e_1_2_1_34_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1882261.1866172","article-title":"Data-driven image color theme enhancement","volume":"29","author":"Wang Baoyuan","year":"2010","unstructured":"Baoyuan Wang , Yizhou Yu , Tien-Tsin Wong , Chun Chen , and Ying-Qing Xu . 2010 . Data-driven image color theme enhancement . ACM Trans. Graph. 29 , 6 (2010), 1 \u2013 10 . Baoyuan Wang, Yizhou Yu, Tien-Tsin Wong, Chun Chen, and Ying-Qing Xu. 2010. Data-driven image color theme enhancement. ACM Trans. Graph. 29, 6 (2010), 1\u201310.","journal-title":"ACM Trans. Graph."},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.404"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2005.864231"},{"key":"e_1_2_1_37_1","doi-asserted-by":"crossref","unstructured":"Lvmin Zhang Chengze Li Tien-Tsin Wong Yi Ji and Chunping Liu. 2018. Two-stage sketch colorization. In ACM Trans. Graph 261.  Lvmin Zhang Chengze Li Tien-Tsin Wong Yi Ji and Chunping Liu. 2018. Two-stage sketch colorization. In ACM Trans. Graph 261.","DOI":"10.1145\/3272127.3275090"},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2671779"},{"key":"e_1_2_1_39_1","article-title":"Real-time user-guided image colorization with learned deep priors","volume":"9","author":"Zhang Richard","year":"2017","unstructured":"Richard Zhang , Jun-Yan Zhu , Phillip Isola , Xinyang Geng , Angela S Lin , Tianhe Yu , and Alexei A Efros . 2017 b. Real-time user-guided image colorization with learned deep priors . ACM Trans. Graph. 9 , 4 (2017). Richard Zhang, Jun-Yan Zhu, Phillip Isola, Xinyang Geng, Angela S Lin, Tianhe Yu, and Alexei A Efros. 2017b. Real-time user-guided image colorization with learned deep priors. ACM Trans. Graph. 9, 4 (2017).","journal-title":"ACM Trans. Graph."}],"container-title":["ACM Transactions on Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3447647","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3447647","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:41:09Z","timestamp":1750200069000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3447647"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,27]]},"references-count":39,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2021,4,30]]}},"alternative-id":["10.1145\/3447647"],"URL":"https:\/\/doi.org\/10.1145\/3447647","relation":{},"ISSN":["0730-0301","1557-7368"],"issn-type":[{"value":"0730-0301","type":"print"},{"value":"1557-7368","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,27]]},"assertion":[{"value":"2020-06-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-01-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-04-27","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}