nach oben

Erschienen in:

2016 | OriginalPaper | Buchkapitel

Learning Representations for Automatic Colorization

verfasst von : Gustav Larsson, Michael Maire, Gregory Shakhnarovich

Erschienen in: Computer Vision – ECCV 2016

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

We develop a fully automatic image colorization system. Our approach leverages recent advances in deep networks, exploiting both low-level and semantic representations. As many scene elements naturally appear according to multimodal color distributions, we train our model to predict per-pixel color histograms. This intermediate output can be used to automatically generate a color image, or further manipulated prior to image formation. On both fully and partially automatic colorization tasks, we outperform existing methods. We also explore colorization as a vehicle for self-supervised visual representation learning.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Learning Recursive Filters for Low-Level Vision via a Hybrid Neural Network

Nächstes Kapitel Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation

Nur mit Berechtigung zugänglich

Note that if the histogram of the L channel were available, it would be possible to match lightness to lightness exactly and thus greatly narrow down color placement.

https://github.com/gustavla/autocolorize.

Bertasius, G., Shi, J., Torresani, L.: Deepedge: a multi-scale bifurcated deep network for top-down contour detection. In: CVPR (2015)

Charpiat, G., Bezrukov, I., Altun, Y., Hofmann, M., Schölkopf, B.: Machine learning methods for automatic image colorization. In: Computational Photography: Methods and Applications. CRC Press (2010)

Charpiat, G., Hofmann, M., Schölkopf, B.: Automatic image colorization via multimodal predictions. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5304, pp. 126–139. Springer, Heidelberg (2008). doi:10.1007/978-3-540-88690-7_10 CrossRef

Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Semantic image segmentation with deep convolutional nets and fully connected CRFs. In: ICLR (2015)

Cheng, Z., Yang, Q., Sheng, B.: Deep colorization. In: ICCV (2015)

Chia, A.Y.S., Zhuo, S., Gupta, R.K., Tai, Y.W., Cho, S.Y., Tan, P., Lin, S.: Semantic colorization with internet images. ACM Trans. Graph. (TOG) 30(6) (2011)

Deshpande, A., Rock, J., Forsyth, D.: Learning large-scale automatic image colorization. In: ICCV (2015)

Doersch, C., Gupta, A., Efros, A.A.: Unsupervised visual representation learning by context prediction. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1422–1430 (2015)

Donahue, J., Krähenbühl, P., Darrell, T.: Adversarial feature learning (2016). arXiv preprint arXiv:1605.09782

10.

Farabet, C., Couprie, C., Najman, L., LeCun, Y.: Learning hierarchical features for scene labeling. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1915–1929 (2013)CrossRef

11.

Ganin, Y., Lempitsky, V.S.: N\({}^{\text{4}}\)-fields: neural network nearest neighbor fields for image transforms. In: ACCV (2014)

12.

Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: AISTATS (2010)

13.

Gupta, R.K., Chia, A.Y.S., Rajan, D., Ng, E.S., Zhiyong, H.: Image colorization using similar images. In: ACM International Conference on Multimedia (2012)

14.

Hariharan, B., an R. Girshick, P.A., Malik, J.: Hypercolumns for object segmentation and fine-grained localization. In: CVPR (2015)

15.

Huang, Y.C., Tung, Y.S., Chen, J.C., Wang, S.W., Wu, J.L.: An adaptive edge detection based colorization algorithm and its applications. In: ACM International Conference on Multimedia (2005)

16.

Iizuka, S., Simo-Serra, E., Ishikawa, H.: Let there be color!: joint end-to-end learning of global and local image priors for automatic image colorization with simultaneous classification. ACM Trans. Graph. (Proc. SIGGRAPH 2016) 35(4) (2016)

17.

Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: ICML (2015)

18.

Ion, A., Carreira, J., Sminchisescu, C.: Probabilistic joint image segmentation and labeling by figure-ground composition. Int. J. Comput. Vision 107(1), 40–57 (2014)MathSciNetCrossRefMATH

19.

Irony, R., Cohen-Or, D., Lischinski, D.: Colorization by example. In: Eurographics Symposium on Rendering (2005)

20.

Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding (2014). arXiv preprint arXiv:1408.5093

21.

Levin, A., Lischinski, D., Weiss, Y.: Colorization using optimization. ACM Trans. Graph. (TOG) 23(3), 689–694 (2004)CrossRef

22.

Liu, W., Rabinovich, A., Berg, A.C.: Parsenet: looking wider to see better (2015). arXiv preprint arXiv:1506.04579

23.

Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR (2015)

24.

Luan, Q., Wen, F., Cohen-Or, D., Liang, L., Xu, Y.Q., Shum, H.Y.: Natural image colorization. In: Eurographics Conference on Rendering Techniques (2007)

25.

Maire, M., Yu, S.X., Perona, P.: Reconstructive sparse code transfer for contour detection and semantic labeling. In: Cremers, D., Reid, I., Saito, H., Yang, M.-H. (eds.) ACCV 2014. LNCS, vol. 9006, pp. 273–287. Springer, Heidelberg (2015). doi:10.1007/978-3-319-16817-3_18

26.

Morimoto, Y., Taguchi, Y., Naemura, T.: Automatic colorization of grayscale images using multiple images on the web. In: SIGGRAPH: Posters (2009)

27.

Mostajabi, M., Yadollahpour, P., Shakhnarovich, G.: Feedforward semantic segmentation with zoom-out features. In: CVPR (2015)

28.

Noroozi, M., Favaro, P.: Unsupervised learning of visual representations by solving jigsaw puzzles (2016). arXiv preprint arXiv:1603.09246

29.

Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., Efros, A.A.: Context encoders: feature learning by inpainting. In: CVPR (2016)

30.

Patterson, G., Xu, C., Su, H., Hays, J.: The sun attribute database: beyond categories for deeper scene understanding. Int. J. Comput. Vision 108(1–2), 59–81 (2014)CrossRef

31.

Qu, Y., Wong, T.T., Heng, P.A.: Manga colorization. ACM Trans. Graph. (TOG) 25(3), 1214–1220 (2006)CrossRef

32.

Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vision (IJCV) 115(3), 211–252 (2015)MathSciNetCrossRef

33.

Sapiro, G.: Inpainting the colors. In: ICIP (2005)

34.

Shen, W., Wang, X., Wang, Y., Bai, X., Zhang, Z.: Deepcontour: a deep convolutional feature learned by positive-sharing loss for contour detection. In: CVPR (2015)

35.

Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 888–905 (2000)CrossRef

36.

Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR (2015)

37.

Sỳkora, D., Buriánek, J., Žára, J.: Unsupervised colorization of black-and-white cartoons. In: International Symposium on Non-Photorealistic Animation and Rendering (2004)

38.

Tai, Y.W., Jia, J., Tang, C.K.: Local color transfer via probabilistic segmentation by expectation-maximization. In: CVPR (2005)

39.

Tola, E., Lepetit, V., Fua, P.: A fast local descriptor for dense matching. In: CVPR (2008)

40.

Tsaftaris, S.A., Casadio, F., Andral, J.L., Katsaggelos, A.K.: A novel visualization tool for art history and conservation: automated colorization of black and white archival photographs of works of art. Stud. Conserv. 59(3), 125–135 (2014)CrossRef

41.

Welsh, T., Ashikhmin, M., Mueller, K.: Transferring color to greyscale images. ACM Trans. Graph. (TOG) 21(3), 277–280 (2002)CrossRef

42.

Xiao, J., Hays, J., Ehinger, K.A., Oliva, A., Torralba, A.: Sun database: large-scale scene recognition from abbey to zoo. In: CVPR (2010)

43.

Xie, S., Tu, Z.: Holistically-nested edge detection. In: ICCV (2015)

44.

Yatziv, L., Sapiro, G.: Fast image and video colorization using chrominance blending. IEEE Trans. Image Process. 15(5), 1120–1129 (2006)CrossRef

45.

Zhang, R., Isola, P., Efros, A.A.: Colorful image colorization. In: ECCV (2016)

Titel: Learning Representations for Automatic Colorization
verfasst von: Gustav Larsson
Michael Maire
Gregory Shakhnarovich
Verlag: Springer International Publishing
Buch: Computer Vision – ECCV 2016
Print ISBN: 978-3-319-46492-3

Electronic ISBN: 978-3-319-46493-0

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-46493-0_35

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"