“A similarity measure for illustration style” by Garces, Agarwala, Gutierrez and Hertzmann

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Title:

    A similarity measure for illustration style

Session/Category Title:   Typography & Illustration


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Abstract:


    This paper presents a method for measuring the similarity in style between two pieces of vector art, independent of content. Similarity is measured by the differences between four types of features: color, shading, texture, and stroke. Feature weightings are learned from crowdsourced experiments. This perceptual similarity enables style-based search. Using our style-based search feature, we demonstrate an application that allows users to create stylistically-coherent clip art mash-ups.

References:


    1. Aucouturier, J.-J., and Pachet, F. 2002. Music Similarity Measures: What’s the Use? In Proc. SMIR, 157–163.Google Scholar
    2. Bae, S., Paris, S., and Durand, F. 2006. Two-scale tone management for photographic look. ACM Trans. Graphics 25, 3, 637–645. Google ScholarDigital Library
    3. Bell, R. M., and Koren, Y. 2007. Lessons from the Netflix Prize Challenge. SIGKDD Explor. Newsl. 9, 2, 75–79. Google ScholarDigital Library
    4. Chen, T., Cheng, M.-M., Tan, P., Shamir, A., and Hu, S.-M. 2009. Sketch2photo: Internet image montage. ACM Trans. Graphics 28, 5, 124:1–124:10. Google ScholarDigital Library
    5. Chen, X., Bennett, P. N., Collins-Thompson, K., and Horvitz, E. 2013. Pairwise Ranking Aggregation in a Crowdsourced Setting. In Proc. WSDM, 193–202. Google ScholarDigital Library
    6. Datta, R., Joshi, D., Li, J., and Wang, J. Z. 2008. Image Retrieval: Ideas, Influences, and Trends of the New Age. ACM Computing Surveys 40, 2, 5:1–5:60. Google ScholarDigital Library
    7. Doersch, C., Singh, S., Gupta, A., Sivic, J., and Efros, A. 2012. What Makes Paris Look like Paris? ACM Trans. Graphics 31, 4, 101:1–101:9. Google ScholarDigital Library
    8. Durand, F. 2002. An Invitation to Discuss Computer Depiction. In Proc. NPAR, 111–124. Google ScholarDigital Library
    9. Epshtein, B., Ofek, E., and Wexler, Y. 2010. Detecting Text in Natural Scenes with Stroke Width Transform. In Proc. CVPR, 2963–2970.Google Scholar
    10. Gooch, B., and Gooch, A. 2001. Non-Photorealistic Rendering. AK Peters Ltd. Google ScholarDigital Library
    11. Haralick, R. M., Shanmugam, K., and Dinstein, I. 1973. Textural Features for Image Classification. IEEE Trans. Systems, Man and Cybernetics 3, 6, 610–621.Google ScholarCross Ref
    12. Hasler, D., and Susstrunk, S. 2003. Measuring Colourfulness in Natural Images. In Proc. SPIE: Human Vision and Electronic Imaging, vol. 5007, 87–95.Google Scholar
    13. Hertzmann, A., Jacobs, C. E., Oliver, N., Curless, B., and Salesin, D. H. 2001. Image Analogies. In Proc. SIGGRAPH, 327–340. Google ScholarDigital Library
    14. Hurtut, T., Gousseau, Y., Cheriet, F., and Schmitt, F. 2011. Artistic line-drawings retrieval based on the pictorial content. ACM J. Computing and Cultural Heritage 4, 1, 1–23. Google ScholarDigital Library
    15. Kalogerakis, E., Nowrouzezahrai, D., Breslav, S., and Hertzmann, A. 2012. Learning Hatching for Pen-and-Ink Illustration of Surfaces. ACM Trans. Graphics 31, 1:1–1:17. Google ScholarDigital Library
    16. Kulis, B. 2013. Metric learning: A survey. Foundations and Trends in Machine Learning 5, 4, 287–364.Google ScholarCross Ref
    17. Lalonde, J.-F., Hoiem, D., Efros, A. A., Rother, C., Winn, J., and Criminisi, A. 2007. Photo clip art. ACM Trans. Graphics 26, 3, 3:1–3:10. Google ScholarDigital Library
    18. Lessig, L. 2008. Remix: Making Art and Commerce Thrive in the Hybrid Economy. Penguin Press.Google ScholarCross Ref
    19. Li, C., Member, S., and Chen, T. 2009. Aesthetic Visual Quality Assessment of Paintings. IEEE J. Sel. Topics in Signal Processing 3, 2, 236–252.Google ScholarCross Ref
    20. Li, H., Zhang, H., Wang, Y., Cao, J., Shamir, A., and Cohen-Or, D. 2013. Curve Style Analysis in a Set of Shapes. Computer Graphics Forum 32, 6, 77–88. Google ScholarDigital Library
    21. McFee, B., and Lanckriet, G. 2011. Learning Multi-modal Similarity. J. Machine Learning Research 12, 491–523. Google ScholarDigital Library
    22. Murray, N., Barcelona, D., Marchesotti, L., and Perronnin, F. 2012. AVA: A Large-Scale Database for Aesthetic Visual Analysis. In Proc. CVPR, 2408–2415. Google ScholarDigital Library
    23. O’Donovan, P., Lībeks, J., Agarwala, A., and Hertzmann, A. 2014. Exploratory Font Selection Using Crowdsourced Attributes. ACM Trans. Graphics 33. Google ScholarDigital Library
    24. Ojala, T., Pietikäinen, M., and Mäenpää, T. 2002. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. PAMI 24, 7, 971–987. Google ScholarDigital Library
    25. Parikh, D., and Grauman, K. 2011. Relative attributes. In Proc. ICCV, 503–510. Google ScholarDigital Library
    26. Radlinski, F., Bennett, P. N., Carterette, B., and Joachims, T. 2009. Redundancy, Diversity and Interdependent Document Relevance. SIGIR Forum 43, 2. Google ScholarDigital Library
    27. Schultz, M., and Joachims, T. 2003. Learning a Distance Metric from Relative Comparisons. In Proc. NIPS.Google Scholar
    28. Shamir, L., Macura, T., Orlov, N., Eckley, D. M., and Goldberg, I. G. 2010. Impressionism, Expressionism, Surrealism: Automated Recognition of Painters and Schools of Art. ACM Trans. Applied Perception 7, 2, 8:1–8:17. Google ScholarDigital Library
    29. Tamuz, O., Liu, C., Belongie, S., Shamir, O., and Kalai, A. 2011. Adaptively Learning the Crowd Kernel. In Proc. ICML, 673–680.Google Scholar
    30. Tenenbaum, J. B., and Freeman, W. T. 2000. Separating Style and Content with Bilinear Models. Neural Computation 12, 6, 1247–1283. Google ScholarDigital Library
    31. van der Maaten, L., and Hinton, G. E. 2008. Visualizing High-Dimensional Data Using t-SNE. J. Machine Learning Research 9, 2579–2605.Google Scholar
    32. Welinder, P., Branson, S., Belongie, S., and Perona, P. 2010. The Multidimensional Wisdom of Crowds. In Proc. NIPS, 2424–2432.Google Scholar
    33. Willats, J., and Durand, F. 2005. Defining pictorial style: Lessons from linguistics and computer graphics. Axiomathes 15, 3, 319–351.Google ScholarCross Ref
    34. Zhu, C., Byrd, R. H., Lu, P., and Nocedal, J. 1997. Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization. ACM Trans. on Mathematical Software 23, 4. Google ScholarDigital Library
    35. Zhu, C., Bichot, C.-E., and Chen, L. 2010. Multi-scale Color Local Binary Patterns for Visual Object Classes Recognition. In Proc. ICPR, 3065–3068. Google ScholarDigital Library


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