Classification and quality assessment of high quality digital photographs

ABSTRACT

We are interested in high quality photographs. This paper outlines our research proposal for the tasks of classification and quality assessment. We address these challenges by exploring the aesthetics from the combined perspectives of the artists and the photographers. We propose to use the aesthetic primitives of images for visualization as a guideline for high and low-level image feature extraction and to classify this high quality content into six creative exposure themes, which are commonly followed by the professional photographers. Then, we suggest to evaluate the quality of the photograph accordingly to these themes. We solve the problems using statistical modeling and learning approach.

REFERENCES

• 1: EXIF Specification. http://www.exif.org.
• 2: Flickr Website. http://www.flickr.com.
• 3: R. Datta, D. Joshi, J. Li, and J. Wang. Studying aesthetics in photographic images using a computational approach. In Proceedings of the ECCV'06, pages III: 288--301, 2006.
• 4: M. J. Huiskes and M. S. Lew. The mir flickr retrieval evaluation. In MIR '08: Proceeding of the 1st ACM international conference on Multimedia information retrieval, pages 39--43, NY, USA, 2008. ACM.
• 5: Y. Ke, X. Tang, and F. Jing. The design of high-level features for photo quality assessment. In Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, volume 1, pages 419--426, 17-22 June 2006.
• 6: C. Li and T. Chen. Aesthetic visual quality assessment of paintings. IEEE Journal of Selected Topics in Signal Processing, 3(2):236--252, April 2009.
• 7: G. Peters. Aesthetic primitives of images for visualization. In Proc. 11th International Conference Information Visualization IV '07, pages 316--325, 4-6 July 2007.
• 8: B. Peterson. Understanding Exposure [Revised Edition]. AMPHOTO Book, 2004.

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.4 IMAGE PROCESSING AND COMPUTER VISION
      I.4.9 Applications

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
        I.2.10 Vision and Scene Understanding

General Terms:
Algorithms, Design, Experimentation

Keywords:
aesthetics, classification, creative exposure theme, feature extraction, photograph, quality assessment