A randomized algorithm for natural object colorization
Natural objects often contain vivid color distribution with wide variety of colors. Conventional colorization techniques, on the other hand, produce colors that are relatively flat with little color variation. In this paper, we introduce a randomized algorithm which considers not only the value of t...
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| Published in: | Computer graphics forum Vol. 33; no. 2; pp. 205 - 214 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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Oxford
Blackwell Publishing Ltd
01.05.2014
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| ISSN: | 0167-7055, 1467-8659 |
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| Abstract | Natural objects often contain vivid color distribution with wide variety of colors. Conventional colorization techniques, on the other hand, produce colors that are relatively flat with little color variation. In this paper, we introduce a randomized algorithm which considers not only the value of target color but also the distribution of target color. In essence, our algorithm paints a color distribution to a region which synthesizes color distribution of a natural object. Our approach models the correlation between intensity and color in HSV color space in terms of H – S, H – V and S – V joint histogram. During the colorization process, we randomly swap and reassign color of a pixel to minimize a cost function that measures color consistency to its neighborhood and intensity‐to‐color correlation captured in the joint histogram. We tested our algorithm extensively on many natural objects and our user study confirms that our results are more vivid and natural compared to results from previous techniques. |
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| AbstractList | Natural objects often contain vivid color distribution with wide variety of colors. Conventional colorization techniques, on the other hand, produce colors that are relatively flat with little color variation. In this paper, we introduce a randomized algorithm which considers not only the value of target color but also the distribution of target color. In essence, our algorithm paints a color distribution to a region which synthesizes color distribution of a natural object. Our approach models the correlation between intensity and color in HSV color space in terms of H – S, H – V and S – V joint histogram. During the colorization process, we randomly swap and reassign color of a pixel to minimize a cost function that measures color consistency to its neighborhood and intensity‐to‐color correlation captured in the joint histogram. We tested our algorithm extensively on many natural objects and our user study confirms that our results are more vivid and natural compared to results from previous techniques. Natural objects often contain vivid color distribution with wide variety of colors. Conventional colorization techniques, on the other hand, produce colors that are relatively flat with little color variation. In this paper, we introduce a randomized algorithm which considers not only the value of target color but also the distribution of target color. In essence, our algorithm paints a color distribution to a region which synthesizes color distribution of a natural object. Our approach models the correlation between intensity and color in HSV color space in terms of H - S, H - V and S - V joint histogram. During the colorization process, we randomly swap and reassign color of a pixel to minimize a cost function that measures color consistency to its neighborhood and intensity-to-color correlation captured in the joint histogram. We tested our algorithm extensively on many natural objects and our user study confirms that our results are more vivid and natural compared to results from previous techniques. [PUBLICATION ABSTRACT] |
| Author | Tai, Yu-Wing Choi, Ho-Jin Jin, Sou-Young |
| Author_xml | – sequence: 1 givenname: Sou-Young surname: Jin fullname: Jin, Sou-Young organization: Korea Advanced Institute of Science and Technology (KAIST) – sequence: 2 givenname: Ho-Jin surname: Choi fullname: Choi, Ho-Jin organization: Korea Advanced Institute of Science and Technology (KAIST) – sequence: 3 givenname: Yu-Wing surname: Tai fullname: Tai, Yu-Wing organization: Korea Advanced Institute of Science and Technology (KAIST) |
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| Cites_doi | 10.1145/987657.987677 10.1145/1882261.1866172 10.1145/1141911.1142017 10.1137/090746379 10.1145/1101149.1101223 10.1609/aaai.v26i1.8274 10.1145/2070781.2024190 10.1109/MCG.2011.18 10.1109/TPAMI.2007.1168 10.1145/1531326.1531375 10.1109/38.946629 10.1145/1409060.1409105 10.1145/1015706.1015780 10.1109/TPAMI.2007.70844 |
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| Copyright | 2014 The Author(s) Computer Graphics Forum © 2014 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd. 2014 The Eurographics Association and John Wiley & Sons Ltd. |
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| Notes | istex:DB9E56AA353B576F35344BE4393EFF3553E1B45D ArticleID:CGF12294 ark:/67375/WNG-NJ5H72R8-F Supporting Information is excluded in the Graph cut implementation. S – Color labels in Hue channel and Saturation channel are solved individually and the nonlocal neighboring term in Equation http://vision.middlebury.edu/MRF/ 4 log We use another color image as an example image because it is closer to practical scenarios. channel is selected since it captures grayscale with 0. is a common method to convert an arg max probability problem into an arg min energy minimization problem We use the middlebury MRF library for the Graph cut implementation SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-2 content type line 23 |
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| References | [TJT07] Tai Y.-W., Jia J., Tang C.-K.: Soft color segmentation and its applications. IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 9 (2007), 1520-1537. 2. [SZS*08] Szeliski R., Zabih R., Scharstein D., Veksler O., Kolmogorov V., Agarwala A., Tappen M., Rother C.: A comparative study of energy minimization methods for markov random fields with smoothness-based priors. IEEE Trans. on PAMI 30, 6 (2008), 1068-1080. 6. [WYW*10] Wang B., Yu Y., Wong T.-T., Chen C., Xu Y.Q.: Data-driven image color theme enhancement. ACM TOG 29, 6 (2010), 146:1-146:10. 2. [SSC*11] Sheng B., Sun H., Chen S., Liu X., Wu E.: Colorization using the rotation-invariant feature space. Computer Graphics and Applications 31, 2 (2011), 24-35. 1, 2. [LSS09] Liu J., Sun J., Shum H.-Y.: Paint selection. ACM TOG 28, 3 (2009), 69:1-69:7. 3. [YS06] Yatziv L., Sapiro G.: Fast image and video colorization using chrominance blending. IEEE TIP 15, 5 (2006), 1120-1129. 1, 2. [HS03] Hasler D., Sãijsstrunk S.: Measuring colourfulness in natural images. IS&T/SPIE Electronic Imaging: Human Vision and Electronic Imaging VIII 5007 (2003), 87-95. 1. [RAGS01] Reinhard E., Ashikhmin M., Gooch B., Shirley P.: Color transfer between images. IEEE Computer Graphics and Applications 21, 5 (2001), 34-41. 2, 4, 7. [WAM02] Welsh T., Ashikhmin M., Mueller K.: Transferring color to greyscale images. ACMTOG 21, 3 (2002), 277-280. 1, 2, 6, 7, 8, 9. [QWH06] Qu Y., Wong T.-T., Heng P.-A.: Manga colorization. ACM TOG 25, 3 (2006), 1214-1220. 1, 2. [CZG*11] Chia Y.S., Zhuo S., Gupta R.K., Tai Y.-W., Cho S.-Y., Tan P., Lin S.: Semantic colorization with internet images. ACM TOG 30, 6 (2011), 156:1-7. 1, 2, 3, 8, 10. [LLW04] Levin A., Lischinski D., Weiss Y.: Colorization using optimization. ACM TOG 23, 3 (2004), 689-694. 1, 2, 4, 6, 7, 8, 9. [LW*08] Liu X., Wan L., Qu Y., Wong T.-T., Lin S., Leung C.-S., Heng P.-A.: Intrinsic colorization. ACM TOG 27, 5 (2008), 152:1-9. 1, 2, 10. 2007; 29 2003; 5007 2001 2012 2010; 29 2010 2004; 23 2008; 27 2006; 25 2002; 21 2006; 15 2011; 31 2008 2011; 30 2007 2005 2004 2008; 30 2009; 28 2001; 21 e_1_2_7_6_1 Hasler D. (e_1_2_7_5_1) 2003; 5007 e_1_2_7_4_1 e_1_2_7_3_1 e_1_2_7_9_1 e_1_2_7_8_1 e_1_2_7_7_1 e_1_2_7_19_1 e_1_2_7_18_1 e_1_2_7_17_1 e_1_2_7_16_1 Yatziv L. (e_1_2_7_23_1) 2006; 15 e_1_2_7_2_1 e_1_2_7_15_1 e_1_2_7_14_1 e_1_2_7_13_1 Wang B. (e_1_2_7_21_1) 2010; 29 e_1_2_7_24_1 e_1_2_7_12_1 e_1_2_7_11_1 e_1_2_7_22_1 e_1_2_7_10_1 Welsh T. (e_1_2_7_20_1) 2002; 21 |
| References_xml | – reference: [LLW04] Levin A., Lischinski D., Weiss Y.: Colorization using optimization. ACM TOG 23, 3 (2004), 689-694. 1, 2, 4, 6, 7, 8, 9. – reference: [TJT07] Tai Y.-W., Jia J., Tang C.-K.: Soft color segmentation and its applications. IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 9 (2007), 1520-1537. 2. – reference: [RAGS01] Reinhard E., Ashikhmin M., Gooch B., Shirley P.: Color transfer between images. IEEE Computer Graphics and Applications 21, 5 (2001), 34-41. 2, 4, 7. – reference: [HS03] Hasler D., Sãijsstrunk S.: Measuring colourfulness in natural images. IS&T/SPIE Electronic Imaging: Human Vision and Electronic Imaging VIII 5007 (2003), 87-95. 1. – reference: [YS06] Yatziv L., Sapiro G.: Fast image and video colorization using chrominance blending. IEEE TIP 15, 5 (2006), 1120-1129. 1, 2. – reference: [WYW*10] Wang B., Yu Y., Wong T.-T., Chen C., Xu Y.Q.: Data-driven image color theme enhancement. ACM TOG 29, 6 (2010), 146:1-146:10. 2. – reference: [QWH06] Qu Y., Wong T.-T., Heng P.-A.: Manga colorization. ACM TOG 25, 3 (2006), 1214-1220. 1, 2. – reference: [LSS09] Liu J., Sun J., Shum H.-Y.: Paint selection. ACM TOG 28, 3 (2009), 69:1-69:7. 3. – reference: [WAM02] Welsh T., Ashikhmin M., Mueller K.: Transferring color to greyscale images. ACMTOG 21, 3 (2002), 277-280. 1, 2, 6, 7, 8, 9. – reference: [CZG*11] Chia Y.S., Zhuo S., Gupta R.K., Tai Y.-W., Cho S.-Y., Tan P., Lin S.: Semantic colorization with internet images. ACM TOG 30, 6 (2011), 156:1-7. 1, 2, 3, 8, 10. – reference: [SSC*11] Sheng B., Sun H., Chen S., Liu X., Wu E.: Colorization using the rotation-invariant feature space. Computer Graphics and Applications 31, 2 (2011), 24-35. 1, 2. – reference: [SZS*08] Szeliski R., Zabih R., Scharstein D., Veksler O., Kolmogorov V., Agarwala A., Tappen M., Rother C.: A comparative study of energy minimization methods for markov random fields with smoothness-based priors. IEEE Trans. on PAMI 30, 6 (2008), 1068-1080. 6. – reference: [LW*08] Liu X., Wan L., Qu Y., Wong T.-T., Lin S., Leung C.-S., Heng P.-A.: Intrinsic colorization. ACM TOG 27, 5 (2008), 152:1-9. 1, 2, 10. – volume: 30 start-page: 1068 year: 2008 end-page: 1080 article-title: A comparative study of energy minimization methods for markov random fields with smoothness‐based priors publication-title: IEEE Trans. on PAMI – volume: 30 start-page: 156:1 year: 2011 end-page: 7 article-title: Semantic colorization with internet images publication-title: ACM TOG – start-page: 327 year: 2001 end-page: 340 – volume: 25 start-page: 1214 year: 2006 end-page: 1220 article-title: Manga colorization publication-title: ACM TOG – volume: 31 start-page: 24 year: 2011 end-page: 35 article-title: Colorization using the rotation‐invariant feature space publication-title: Computer Graphics and Applications – start-page: 121 year: 2004 end-page: 127 – start-page: 201 year: 2005 end-page: 210 – volume: 27 start-page: 152:1 year: 2008 end-page: 9 article-title: Intrinsic colorization publication-title: ACM TOG – start-page: 1169 year: 2012 end-page: 1175 – volume: 28 start-page: 69:1 year: 2009 end-page: 69:7 article-title: Paint selection publication-title: ACM TOG – year: 2008 – volume: 5007 start-page: 87 year: 2003 end-page: 95 article-title: Measuring colourfulness in natural images publication-title: IS&T/SPIE Electronic Imaging: Human Vision and Electronic Imaging VIII – volume: 15 start-page: 1120 year: 2006 end-page: 1129 article-title: Fast image and video colorization using chrominance blending publication-title: IEEE TIP – volume: 21 start-page: 34 year: 2001 end-page: 41 article-title: Color transfer between images publication-title: IEEE Computer Graphics and Applications – volume: 21 start-page: 277 year: 2002 end-page: 280 article-title: Transferring color to greyscale images publication-title: ACMTOG – start-page: 747 year: 2005 end-page: 754 – start-page: 309 year: 2007 end-page: 320 – volume: 29 start-page: 146:1 year: 2010 end-page: 146:10 article-title: Data‐driven image color theme enhancement publication-title: ACM TOG – start-page: 253 year: 2010 end-page: 276 – volume: 29 start-page: 1520 year: 2007 end-page: 1537 article-title: Soft color segmentation and its applications publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence – start-page: 351 year: 2005 end-page: 354 – year: 2012 – volume: 23 start-page: 689 year: 2004 end-page: 694 article-title: Colorization using optimization publication-title: ACM TOG – ident: e_1_2_7_8_1 – ident: e_1_2_7_15_1 doi: 10.1145/987657.987677 – volume: 29 start-page: 146:1 year: 2010 ident: e_1_2_7_21_1 article-title: Data‐driven image color theme enhancement publication-title: ACM TOG doi: 10.1145/1882261.1866172 – ident: e_1_2_7_2_1 – ident: e_1_2_7_13_1 doi: 10.1145/1141911.1142017 – ident: e_1_2_7_24_1 doi: 10.1137/090746379 – ident: e_1_2_7_6_1 doi: 10.1145/1101149.1101223 – ident: e_1_2_7_22_1 doi: 10.1609/aaai.v26i1.8274 – volume: 15 start-page: 1120 year: 2006 ident: e_1_2_7_23_1 article-title: Fast image and video colorization using chrominance blending publication-title: IEEE TIP – ident: e_1_2_7_3_1 doi: 10.1145/2070781.2024190 – ident: e_1_2_7_16_1 doi: 10.1109/MCG.2011.18 – ident: e_1_2_7_19_1 doi: 10.1109/TPAMI.2007.1168 – volume: 21 start-page: 277 year: 2002 ident: e_1_2_7_20_1 article-title: Transferring color to greyscale images publication-title: ACMTOG – ident: e_1_2_7_10_1 doi: 10.1145/1531326.1531375 – ident: e_1_2_7_14_1 doi: 10.1109/38.946629 – volume: 5007 start-page: 87 year: 2003 ident: e_1_2_7_5_1 article-title: Measuring colourfulness in natural images publication-title: IS&T/SPIE Electronic Imaging: Human Vision and Electronic Imaging VIII – ident: e_1_2_7_18_1 – ident: e_1_2_7_11_1 doi: 10.1145/1409060.1409105 – ident: e_1_2_7_4_1 – ident: e_1_2_7_7_1 – ident: e_1_2_7_9_1 doi: 10.1145/1015706.1015780 – ident: e_1_2_7_12_1 – ident: e_1_2_7_17_1 doi: 10.1109/TPAMI.2007.70844 |
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| SubjectTerms | Algorithms Analysis Categories and Subject Descriptors (according to ACM CCS) Color Colorization Computer graphics Consistency Correlation Correlation analysis Histograms I.3.8 [Computer Graphics]: Applications Pixels Studies |
| Title | A randomized algorithm for natural object colorization |
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