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OSCAR: On-Site Composition and Aesthetics Feedback Through Exemplars for Photographers

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Abstract

In this paper we describe a comprehensive system to enhance the aesthetic quality of the photographs captured by the mobile consumers. The system, named OSCAR, has been designed to provide on-site composition and aesthetics feedback through retrieved examples. We introduce three novel interactive feedback components. The first is the composition feedback which is qualitative in nature and responds by retrieving highly aesthetic exemplar images from the corpus which are similar in content and composition to the snapshot. The second is the color combination feedback which provides confidence on the snapshot to contain good color combinations. The third component is the overall aesthetics feedback which predicts the aesthetic ratings for both color and monochromatic images. An existing algorithm is used to provide ratings for color images, while new features and a new model are developed to treat monochromatic images. This system was designed keeping the next generation photography needs in mind and is the first of its kind. The feedback rendered is guiding and intuitive in nature. It is computed in situ while requiring minimal input from the user.

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References

  • Bhattacharya, S., Sukthankar, R., & Shah, M. (2010). A coherent framework for photo-quality assessment and enhancement based on visual aesthetics. In Proceedings of ACM multimedia conference (pp. 271–280).

    Google Scholar 

  • Cohen-Or, D., Sorkine, O., Gal, R., Leyvand, T., & Xu, Y. (2006). Color harmonization. ACM Transactions on Graphics, 25(3), 624–630.

    Article  Google Scholar 

  • Datta, R., Joshi, D., Li, J., & Wang, J. Z. (2006). Studying aesthetics in photographic images using a computational approach. In Proceedings of European conference on computer vision (pp. 288–301).

    Google Scholar 

  • Datta, R., & Wang, J. Z. (2010). ACQUINE: aesthetic quality inference engine—real-time automatic rating of photo aesthetics. In Proceedings of international conference on multimedia information retrieval (pp. 421–424).

    Google Scholar 

  • Davis, H. (2010). Creative black and white. Digital photography tips and techniques. New York: Wiley.

    Google Scholar 

  • Feininger, A. (1973). Principles of composition in photography. London: Thames and Hudson.

    Google Scholar 

  • Folts, J. A., Lovell, R. P., & Zwahlen, F. C. (2005). Handbook of photography. New York: Thompson Delmar Learning.

    Google Scholar 

  • Fogarty, J., Forlizzi, J., & Hudson, S. E. (2001). Aesthetic information collages: generating decorative displays that contain information. In Proceedings of ACM symposium on user interface software and technology (pp. 141–150).

    Google Scholar 

  • Gao, X., Xin, J., Sato, T., Hansuebsai, A., Scalzo, M., Kajiwara, K., Guan, S., Valldeperas, J., Lis, M., & Billger, M. (2007). Analysis of cross-cultural color emotion. Color Research and Application, 32(3), 223–229.

    Article  Google Scholar 

  • Gersho, A. (1979). Asymptotically optimal block quantization. IEEE Transactions on Information Theory, 25(4), 373–380.

    Article  MathSciNet  MATH  Google Scholar 

  • Gill, M. (2000). Color harmony pastels—a guidebook for creating great color combinations. Minneapolis: Rockport Publisher.

    Google Scholar 

  • Harel, J., Koch, C., & Perona, P. (2007). Graph-based visual saliency. In Proceedings of advances in neural information processing systems (pp. 542–552).

    Google Scholar 

  • Henri, R. (2007). The art spirit. New York: Basic Books.

    Google Scholar 

  • Itten, J. (1960). The art of color. New York: Van Nostrand Reinhold.

    Google Scholar 

  • Karatzoglou, A., Smola, A., Hornik, K., & Zeileis, A. (2004). Kernlab—a package for kernel methods in R. Journal of Statistical Software, 11(9), 1–20.

    Google Scholar 

  • Ke, Y., Tang, X., & Jing, F. (2006). The design of high-level features for photo quality assessment. In Proceedings of IEEE conference on computer vision and pattern recognition (pp. 419–426).

    Google Scholar 

  • Kisilev, P., Shaked, D., & Lim, S. (2007). Noise and signal activity maps for better imaging algorithms. In Proceedings of IEEE international conference on image processing (pp. 117–120).

    Google Scholar 

  • Krages, B. P. (2005). Photography: the art of composition. New York: Allworth Press.

    Google Scholar 

  • Lamb, J., & Stevens, R. (2010). Eye of the photographer. The Social Studies Texan, 26(1), 59–63.

    Google Scholar 

  • Levina, E., & Bickel, P. (2001). The Earth Mover’s distance is the Mallows distance: some insights from statistics. In Proceedings of international conference on computer vision (pp. 251–256).

    Google Scholar 

  • Li, J. (2011). Agglomerative connectivity constrained clustering for image segmentation. Statistical Analysis and Data Mining, 4(1), 84–99.

    Article  MathSciNet  Google Scholar 

  • Li, J., Ray, S., & Lindsay, B. G. (2007). A nonparametric statistical approach to clustering via mode identification. Journal of Machine Learning Research, 8(8), 1687–1723.

    MathSciNet  MATH  Google Scholar 

  • Li, J., Wang, J. Z., & Wiederhold, G. (2000). IRM: integrated region matching for image retrieval. In Proceedings of ACM multimedia conference (pp. 147–156).

    Google Scholar 

  • Liu, L., Chen, R., Wolf, L., & Cohen-Or, D. (2010). Optimizing photo composition. Computer Graphics Forum, 29(2), 469–478.

    Article  Google Scholar 

  • Luo, Y., & Tang, X. (2008). Photo and video quality evaluation: focusing on the subject. In Proceedings of European conference on computer vision (pp. 386–399).

    Google Scholar 

  • Mallows, C. L. (1972). A note on asymptotic joint normality. Annals of Mathematical Statistics, 43(2), 508–515.

    Article  MathSciNet  MATH  Google Scholar 

  • Manav, B. (2007). Color-emotion associations and color preferences: a case study for residences. Color Research and Application, 32(3), 144–150.

    Article  Google Scholar 

  • Meer, P., & Georgescu, B. (2001). Edge detection with embedded confidence. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(12), 1351–1365.

    Article  Google Scholar 

  • Obrador, P., Anguera, X., Oliveira, R., & Oliver, N. (2009). The role of tags and image aesthetics in social image search. In Proceedings of ACM SIGMM workshop on social media (pp. 65–72).

    Chapter  Google Scholar 

  • Obrador, P., Oliveira, R., & Oliver, N. (2010). Supporting personal photo storytelling for social albums. In Proceedings of ACM multimedia conference (pp. 561–570).

    Google Scholar 

  • Peters, G. (2007). Aesthetic primitives of images for visualization. In Proceedings of IEEE international conference on information visualization (pp. 316–326).

    Google Scholar 

  • Rubner, Y., Tomasi, C., & Guibas, L. J. (2000). The Earth Mover’s distance as a metric for image retrieval. International Journal of Computer Vision, 40(2), 99–121.

    Article  MATH  Google Scholar 

  • Russ, J. C. (2006). The image processing handbook. Boca Raton: CRC Press.

    Book  Google Scholar 

  • Speed, H. (1972). The practice and science of drawing (3rd ed.). New York: Dover.

    Google Scholar 

  • Sternberg, R. J. (2008). Cognitive psychology. Belmont: Wadsworth Publishing.

    Google Scholar 

  • Suess, B. J. (1995). Mastering black and white photography: from camera to darkroom. New York: Allworth Press.

    Google Scholar 

  • Sutton, T., & Whelan, B. M. (2004). The complete color harmony. Minneapolis: Rockport Publisher.

    Google Scholar 

  • Taylor, M., Butler, O., & Birnbaum, H. (1998). Kodak workshop series: advanced black-and-white photograph. New York: Sterling Publishing.

    Google Scholar 

  • Tokumaru, M., Muranaka, N., & Imanishi, S. (2002). Color design support system considering color harmony. In Proceedings of IEEE international conference on fuzzy systems (pp. 378–383).

    Google Scholar 

  • Tong, H., Li, M., Zhang, H., & Zhang, C. (2004). Blur detection for digital images using wavelet transform. In Proceedings of IEEE international conference on multimedia & expo (pp. 17–20).

    Google Scholar 

  • Wang, J. Z., Li, J., & Wiederhold, G. (2001). SIMPLIcity: semantics-sensitive integrated matching for picture libraries. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(9), 947–963.

    Article  Google Scholar 

  • Wong, L., & Low, K. (2009). Saliency-enhanced image aesthetics class prediction. In Proceedings of IEEE international conference on image processing (pp. 993–996).

    Google Scholar 

  • Warren, B. (2002). Photography: the concise guide. New York: Delmar Cengage Learning.

    Google Scholar 

Download references

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Correspondence to Lei Yao.

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Yao, L., Suryanarayan, P., Qiao, M. et al. OSCAR: On-Site Composition and Aesthetics Feedback Through Exemplars for Photographers. Int J Comput Vis 96, 353–383 (2012). https://doi.org/10.1007/s11263-011-0478-3

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  • DOI: https://doi.org/10.1007/s11263-011-0478-3

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