Abstract
This paper is motivated by a book in which artists and illustrators from all over the world offer their personal interpretations of the declaration of human rights in pictures [1]. It was enthusiastic for a young reader to see an illustration of an artist that he already knows from his books . The characters were different, the topic was irrelevant, but still it was easy to identify the illustrators based on the style of the illustration. Inspired by the human’s ability to identify illustrators, in this study we propose a method that can automatically learn to distinguish illustrations of different illustrators using computer vision techniques.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
We Are All Born Free: The Universal Declaration of Human Rights in Pictures. Frances Lincoln (2008)
Stork, D.: Computer image analysis of paintings and drawings: an introduction to the literature. In: The 1st International Workshop on Image Processing for Artist Identification, Amsterdam, The Netherlands (2008)
Sablatnig, R., Kammerer, P., Zolda, E.: Hierarchical classification of paintings using face- and brush stroke models. In: 14th International Conference on Pattern Recognition, vol. 1, pp. 172–174 (1998)
Kroner, S., Lattner, A.: Authentication of free hand drawings by pattern recognition methods. In: 14th International Conference on Pattern Recognition, vol. 1, pp. 462–464 (1998)
Keren, D.: Painter identification using local features and naive bayes. In: 16th International Conference on Pattern Recognition, vol. 2, pp. 474–477 (2002)
Icoglu, O., Gunsel, B., Sariel, S.: Classification and indexing of paintings based on art movements. In: Proceedings of European Signal Processing Conference (EUSIPCO), Vienna, Austria, pp. 749–752 (2004)
Lombardi, T.: The Classification of Style in Painting: Computational Approaches to Artistic Style. VDM Verlag (2008)
Legrand, A., Vurpillot, V., Tremeau, A., Schettini, R.: Automatic color patch selection for painting identification. In: 4th European Conference on Colour in Graphics, Imaging, and Vision (CGIV), pp. 300–303 (2008)
Zujovic, J., Gandy, L., Friedman, S., Pardo, B., Pappas, T.N.: Classifying paintings by artistic genre: An analysis of features and classifiers. In: Proceedings of IEEE International Workshop on Multimedia Signal Processing (MMSP), Rio de Janeiro, Brazil (2009)
Antaresti, T., Arymurthy, A.M.: Image feature extraction and recognition of abstractionism and realism style of indonesian paintings. In: Proceedings of the 2010 Second International Conference on Advances in Computing, Control, and Telecommunication Technologies (ACT 2010), Washington, DC, USA, pp. 149–152 (2010)
Blessing, A., Wen, K.: Using machine learning for identification of art paintings. Technical report, Stanford University (2010)
Johnson, C.R., Hendriks, J.E., Berezhnoy, I.J., Brevdo, E., Hughes, S., Daubechies, I., Li, J., Postma, E., Wang, J.Z.: Image processing for artist identification. IEEE Signal Processing Magazine, 37–48 (2008)
Cabral, R., Costeira, J.P., la Torre, F.D., Bernardino, A., Carneiro, G.: Time and order estimation of paintings based on visual features and expert priors. In: Proc. of the Conference on Computer Vision and Analysis of Images of Art II, San Francisco, USA (2011)
da Silva, N.P., Marques, M., Carneiro, G., Costeira, J.P.: Explaining scene composition using kinematic chains of humans: application to portuguese tiles history. In: Proc. of the Conference on Computer Vision and Analysis of Images of Art II, San Francisco, USA (2011)
Roman-Rangel, E., Pallan, C., Odobez, J.M., Gatica-Perez, D.: Analyzing ancient maya glyph collections with contextual shape descriptors. Int. Journal of Computer Vision, Special Issue on e-Heritage 94, 101–117 (2011)
Oliva, A., Torralba, A.: Modeling the shape of the scene: A holistic representation of the spatial envelope. International Journal of Computer Vision 42, 145–175 (2001)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Schmid, C., Soatto, S., Tomasi, C. (eds.) International Conference on Computer Vision & Pattern Recognition, vol. 2, INRIA Rhône-Alpes, ZIRST-655, av. de l’Europe, Montbonnot-38334, pp. 886–893 (2005)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60, 91–110 (2004)
Sivic, J., Russell, B.C., Efros, A.A., Zisserman, A., Freeman, W.T.: Discovering object categories in image collections. In: Proceedings of the International Conference on Computer Vision (2005)
Chang, C.C., Lin, C.J.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 27:1–27:27 (2011), Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sener, F., Samet, N., Sahin, P.D. (2012). Identification of Illustrators. In: Fusiello, A., Murino, V., Cucchiara, R. (eds) Computer Vision – ECCV 2012. Workshops and Demonstrations. ECCV 2012. Lecture Notes in Computer Science, vol 7583. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33863-2_61
Download citation
DOI: https://doi.org/10.1007/978-3-642-33863-2_61
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33862-5
Online ISBN: 978-3-642-33863-2
eBook Packages: Computer ScienceComputer Science (R0)