Content-based photo quality assessment - Versionsgeschichte

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== Referenz ==
Luo, W., Wang, X., Tang, X.: [[Content-based photo quality assessment]]. In: Computer Vision (ICCV), 2011 IEEE International Conference on, IEEE (2011) 2206-2213

== DOI ==
http://dx.doi.org/10.1109/ICCV.2011.6126498

== Abstract ==
Automatically assessing photo quality from the perspective of visual aesthetics is of great interest in high-level vision research and has drawn much attention in recent years. In this paper, we propose content-based photo quality assessment using regional and global features. Under this framework, subject areas, which draw the most attentions of human eyes, are first extracted. Then regional features extracted from subject areas and the background regions are combined with global features to assess the photo quality. Since professional photographers may adopt different photographic techniques and may have different aesthetic criteria in mind when taking different types of photos (e.g. landscape versus portrait), we propose to segment regions and extract visual features in different ways according to the categorization of photo content. Therefore we divide the photos into seven categories based on their content and develop a set of new subject area extraction methods and new visual features, which are specially designed for different categories. This argument is supported by extensive experimental comparisons of existing photo quality assessment approaches as well as our new regional and global features over different categories of photos. Our new features significantly outperform the state-of-the-art methods. Another contribution of this work is to construct a large and diversified benchmark database for the research of photo quality assessment. It includes 17, 613 photos with manually labeled ground truth.

== Extended Abstract ==

== Bibtex ==
@INPROCEEDINGS{6126498,
author={Wei Luo and Xiaogang Wang and X. Tang},
booktitle={2011 International Conference on Computer Vision},
title={Content-based photo quality assessment},
year={2011},
pages={2206-2213},
keywords={computer vision;feature extraction;image segmentation;background regions;content-based photo quality assessment;global features;high-level vision research;region segmentation;regional feature extraction;subject area extraction methods;visual aesthetics;visual feature extraction;Brightness;Feature extraction;Humans;Image color analysis;Layout;Lighting;Quality assessment},
doi={10.1109/ICCV.2011.6126498},
url={http://dx.doi.org/10.1109/ICCV.2011.6126498 http://de.evo-art.org/index.php?title=Content-based_photo_quality_assessment},
ISSN={1550-5499},
month={Nov},
}

== Used References ==
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J. Carucci. Capturing the Night with Your Camera: How to Take Great Photographs After Dark. Amphoto, 1995. 1

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N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In Proc. CVPR, 2005. 5
Abstract | Full Text: PDF (241KB) | Full Text: HTML

R. Datta, D. Joshi, J. Li, and J. Wang. Studying aesthetics in photographic images using a computational approach. In Proc. ECCV, 2006. 1, 3, 6, 7

C. Grey. Master Lighting Guide for Portrait Photographers. Amherst Media, Inc., 2004. 1

K. He, J. Sun, and X. Tang. Single image haze removal using dark channel prior. In Proc. CVPR, 2009. 5
Abstract | Full Text: PDF (3014KB) | Full Text: HTML

K. He, J. Sun, and X. Tang. Single image haze removal using dark channel prior. IEEE Trans. on PAMI, 2010. 5

D. Hoiem, A. Efros, and M. Hebert. Recovering surface layout from an image. Int'l Journal of Computer Vision, 2007. 4, 5
[CrossRef]

X. Jin, M. Zhao, X. Chen, Q. Zhao, and S. Zhu. Learning Artistic Lighting Template from Portrait Photographs. In Proc. ECCV, 2010. 1
[CrossRef]

Y. Ke, X. Tang, and F. Jing. The design of high-level features for photo quality assessment. In Proc. CVPR, 2006. 1, 2, 6, 7
Abstract | Full Text: PDF (936KB) | Full Text: HTML

Y. Luo and X. Tang. Photo and video quality evaluation: Focusing on the subject. In Proc. ECCV, 2008. 1, 3, 4, 5, 6, 7

H. Mante and E. Linssen. Color design in photography. Focal Press, 1972. 3

M. Nishiyama, T. Okabe, Y. Sato, and I. Sato. Sensationbased photo cropping. In Proc. ACM MM, 2009. 3

X. Ren and J. Malik. Learning a classification model for segmentation. In Proc. ICCV, 2003. 5
Abstract | Full Text: PDF (855KB) | Full Text: HTML

A. Savakis and S. Etz. Method for automatic assessment of emphasis and appeal in consumer images, Dec. 30 2003. US Patent 6,671,405. 1

M. Tokumaru, N. Muranaka, and S. Imanishi. Color design support system considering color harmony. In Proc. IEEE International Conference on Fuzzy Systems, 2002. 3
Abstract | Full Text: PDF (449KB) | Full Text: HTML

H. Tong, M. Li, H. Zhang, J. He, and C. Zhang. Classification of digital photos taken by photographers or home users. In Proc. PCM, 2004. 1, 2

L.White. Infrared Photography Handbook. AmherstMedia, Inc., 1995. 1

L. Wong and K. Low. Saliency-enhanced image aesthetics class prediction. In Proc. ICIP, 2009. 1, 3
Abstract | Full Text: PDF (285KB)

R. Xiao, H. Zhu, H. Sun, and X. Tang. Dynamic cascades for face detection. In Proc. ICCV, 2007. 5
Abstract | Full Text: PDF (559KB) | Full Text: HTML

== Links ==

=== Full Text ===

[[internal file]]

=== Sonstige Links ===

@@ Zeile 33: / Zeile 33: @@
 D. Cohen-Or, O. Sorkine, R. Gal, T. Leyvand, and Y. Xu. Color harmonization. In Proc. ACM SIGGRAPH, 2006. 3, 4 http://dx.doi.org/10.1145/1179352.1141933
-N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In Proc. CVPR, 2005. 5
+N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In Proc. CVPR, 2005. 5 http://dx.doi.org/10.1109/CVPR.2005.177
 R. Datta, D. Joshi, J. Li, and J. Wang. Studying aesthetics in photographic images using a computational approach. In Proc. ECCV, 2006. 1, 3, 6, 7
@@ Zeile 40: / Zeile 39: @@
 C. Grey. Master Lighting Guide for Portrait Photographers. Amherst Media, Inc., 2004. 1
-K. He, J. Sun, and X. Tang. Single image haze removal using dark channel prior. In Proc. CVPR, 2009. 5
+K. He, J. Sun, and X. Tang. Single image haze removal using dark channel prior. In Proc. CVPR, 2009. 5 http://dx.doi.org/10.1109/CVPR.2009.5206515
 K. He, J. Sun, and X. Tang. Single image haze removal using dark channel prior. IEEE Trans. on PAMI, 2010. 5
-D. Hoiem, A. Efros, and M. Hebert. Recovering surface layout from an image. Int'l Journal of Computer Vision, 2007. 4, 5
+D. Hoiem, A. Efros, and M. Hebert. Recovering surface layout from an image. Int'l Journal of Computer Vision, 2007. 4, 5 http://dx.doi.org/10.1007/s11263-006-0031-y
-X. Jin, M. Zhao, X. Chen, Q. Zhao, and S. Zhu. Learning Artistic Lighting Template from Portrait Photographs. In Proc. ECCV, 2010. 1
+X. Jin, M. Zhao, X. Chen, Q. Zhao, and S. Zhu. Learning Artistic Lighting Template from Portrait Photographs. In Proc. ECCV, 2010. 1 http://dx.doi.org/10.1007/978-3-642-15561-1_8
-Y. Ke, X. Tang, and F. Jing. The design of high-level features for photo quality assessment. In Proc. CVPR, 2006. 1, 2, 6, 7
+Y. Ke, X. Tang, and F. Jing. The design of high-level features for photo quality assessment. In Proc. CVPR, 2006. 1, 2, 6, 7 http://dx.doi.org/10.1109/CVPR.2006.303
 Y. Luo and X. Tang. Photo and video quality evaluation: Focusing on the subject. In Proc. ECCV, 2008. 1, 3, 4, 5, 6, 7
@@ Zeile 60: / Zeile 55: @@
 M. Nishiyama, T. Okabe, Y. Sato, and I. Sato. Sensationbased photo cropping. In Proc. ACM MM, 2009. 3
-X. Ren and J. Malik. Learning a classification model for segmentation. In Proc. ICCV, 2003. 5
+X. Ren and J. Malik. Learning a classification model for segmentation. In Proc. ICCV, 2003. 5 http://dx.doi.org/10.1109/ICCV.2003.1238308
 A. Savakis and S. Etz. Method for automatic assessment of emphasis and appeal in consumer images, Dec. 30 2003. US Patent 6,671,405. 1
-M. Tokumaru, N. Muranaka, and S. Imanishi. Color design support system considering color harmony. In Proc. IEEE International Conference on Fuzzy Systems, 2002. 3
+M. Tokumaru, N. Muranaka, and S. Imanishi. Color design support system considering color harmony. In Proc. IEEE International Conference on Fuzzy Systems, 2002. 3 http://dx.doi.org/10.1109/FUZZ.2002.1005020
 H. Tong, M. Li, H. Zhang, J. He, and C. Zhang. Classification of digital photos taken by photographers or home users. In Proc. PCM, 2004. 1, 2
@@ Zeile 72: / Zeile 65: @@
 L.White. Infrared Photography Handbook. AmherstMedia, Inc., 1995. 1
-L. Wong and K. Low. Saliency-enhanced image aesthetics class prediction. In Proc. ICIP, 2009. 1, 3
+L. Wong and K. Low. Saliency-enhanced image aesthetics class prediction. In Proc. ICIP, 2009. 1, 3 http://dx.doi.org/10.1109/ICIP.2009.5413825
-R. Xiao, H. Zhu, H. Sun, and X. Tang. Dynamic cascades for face detection. In Proc. ICCV, 2007. 5
+R. Xiao, H. Zhu, H. Sun, and X. Tang. Dynamic cascades for face detection. In Proc. ICCV, 2007. 5 http://dx.doi.org/10.1109/ICCV.2007.4409043
 == Links ==

@@ Zeile 81: / Zeile 81: @@
 === Full Text ===
+http://www.ee.cuhk.edu.hk/~xgwang/papers/tangLWtmm13.pdf
 [[internal file]]