Rating Image Aesthetics Using Deep Learning: Unterschied zwischen den Versionen

Aus de_evolutionary_art_org
Wechseln zu: Navigation, Suche
(Die Seite wurde neu angelegt: „== Reference == Lu, X.; Lin, Z.; Jin, H.; Yang, J.; Wang, J.Z.: Rating Image Aesthetics Using Deep Learning. IEEE Transactions on Multimedia, 2015, Volume:…“)
 
Zeile 27: Zeile 27:
  
 
== Used References ==
 
== Used References ==
−
References are not available for this document.
+
R. Datta, D. Joshi, J. Li and J. Wang, "Studying aesthetics in photographic images using a computational approach", Proc. Eur. Conf. Comput. Vis., pp. 288-301
  
 +
Y. Ke, X. Tang and F. Jing, "The design of high-level features for photo quality assessment", Proc. IEEE Conf. Comput. Vis. Pattern Recog., vol. 1, pp. 419-426
 +
 +
Y. Luo and X. Tang, "Photo and video quality evaluation: Focusing on the subject", Proc. Eur. Conf. Comput. Vis., pp. 386-399 http://dx.doi.org/10.1007/978-3-540-88690-7_29
 +
 +
S. Bhattacharya, R. Sukthankar and M. Shah, "A framework for photo-quality assessment and enhancement based on visual aesthetics", Proc. ACM Int. Conf. Multimedia, pp. 271-280 [CrossRef]
 +
 +
W. Luo, X. Wang and X. Tang, "Content-based photo quality assessment", Proc. IEEE Int. Conf. Comput. Vis., pp. 2206-2213
 +
 +
S. Dhar, V. Ordonez and T. Berg, "High level describable attributes for predicting aesthetics and interestingness", Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1657-1664
 +
Abstract | Full Text: PDF (4274KB) | Full Text: HTML
 +
 +
M. Nishiyama, T. Okabe, I. Sato and Y. Sato, "Aesthetic quality classification of photographs based on color harmony", Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 33-40
 +
Abstract | Full Text: PDF (2660KB) | Full Text: HTML
 +
 +
P. O¿¿¿Donovan, A. Agarwala and A. Hertzmann, "Color compatibility from large datasets", ACM Trans. Graph., vol. 30, no. 4, pp. 63:1-63:12, 2011 [CrossRef]
 +
 +
L. Marchesotti, F. Perronnin, D. Larlus and G. Csurka, "Assessing the aesthetic quality of photographs using generic image descriptors", Proc. IEEE Int. Conf. Comput. Vis., pp. 1784-1791
 +
Abstract | Full Text: PDF (6768KB) | Full Text: HTML
 +
 +
N. Murray, L. Marchesotti and F. Perronnin, "AVA: A large-scale database for aesthetic visual analysis", Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 2408-2415
 +
Abstract | Full Text: PDF (1112KB) | Full Text: HTML
 +
 +
L. Marchesotti and F. Perronnin, "Learning beautiful (and ugly) attributes", Proc. Brit. Mach. Vis. Conf., pp. 7.1-7.11
 +
 +
D. Lowe, "Distinctive image features from scale-invariant keypoints", Int. J. Comput. Vis., vol. 60, no. 2, pp. 91-110, 2004 [CrossRef]
 +
 +
A. Krizhevsky, I. Sutskever and G. E. Hinton, "Imagenet classification with deep convolutional neural networks", Proc. Adv. Neural Inf. Process. Syst., pp. 1106-1114
 +
 +
H.-H. Su, T.-W. Chen, C.-C. Kao, W. Hsu and S.-Y. Chien, "Scenic photo quality assessment with bag of aesthetics-preserving features", Proc. ACM Int. Conf. Multimedia, pp. 1213-1216 [CrossRef]
 +
 +
A. Oliva and A. Torralba, "Modeling the shape of the scene: A holistic representation of the spatial envelope", Int. J. Comput. Vis., vol. 42, no. 3, pp. 145-175, 2001 [CrossRef]
 +
 +
D. Ciresan, U. Meier and J. Schmidhuber, "Multi-column deep neural networks for image classification", Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3642-3649
 +
Abstract | Full Text: PDF (648KB) | Full Text: HTML
 +
 +
Y. Sun, X. Wang and X. Tang, "Hybrid deep learning for face verification", Proc. IEEE Int. Conf. Comput. Vis., pp. 1489-1496
 +
Abstract | Full Text: PDF (824KB) | Full Text: HTML
 +
 +
P. Sermanet, K. Kavukcuoglu, S. Chintala and Y. LeCun, "Pedestrian detection with unsupervised multi-stage features learning", Proc. IEEE Conf. Computer Vis. Pattern Recog., pp. 3626-3633
 +
Abstract | Full Text: PDF (585KB)
 +
 +
Y. LeCun, L. Bottou, Y. Bengio and P. Haffner, "Gradient-based learning applied to document recognition", Proc. IEEE, vol. 86, no. 11, pp. 2278-2324, 1998
 +
Abstract | Full Text: PDF (896KB)
 +
 +
G. E. Hinton, S. Osindero and Y.-W. Teh, "A fast learning algorithm for deep belief nets", Neural Comput., vol. 18, no. 7, pp. 1527-1554, 2006 [CrossRef]
 +
 +
G. Hinton, "Training products of experts by minimizing contrastive divergence", Neural Comput., vol. 14, no. 8, pp. 1771-1800, 2002 [CrossRef]
 +
 +
S. Karayev, A. Hertzmann, H. Winnermoller, A. Agarwala and T. Darrel, "Recognizing image style", Proc. Brit. Mach. Vis. Conf. [CrossRef]
 +
 +
J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng and T. Darrell, DeCAF: A deep convolutional activation feature for generic visual recognition, 2013
 +
 +
F. Agostinelli, M. Anderson and H. Lee, "Adaptive multi-column deep neural networks with application to robust image denoising", Proc. Adv. Neural Inf. Process. Syst., pp. 1493-1501
 +
 +
A. Khosla, A. Das Sarma and R. Hamid, "What makes an image popular?", Proc. Int. World Wide Web Conf., pp. 867-876 [CrossRef]
 +
 +
O. Litzel, On Photographic Composition, 1974, Amphoto
 +
 +
W. Niekamp, "An exploratory investigation into factors affecting visual balance", Educational Commun. Technol. A, J. Theory, Res., Develop., vol. 29, no. 1, pp. 37-48, 1981
 +
 +
R. Arnheim, Art and Visual Perception: A Psychology of the Creative Eye, 1974, Univ. of California
 +
 +
D. Joshi, R. Datta, E. Fedorovskaya, Q. T. Luong, J. Z. Wang, J. Li and J. B. Luo, "Aesthetics and emotions in images", IEEE Signal Process. Mag., vol. 28, no. 5, pp. 94-115, 2011
 +
Abstract | Full Text: PDF (3585KB) | Full Text: HTML
 +
 +
J. Pan and Q. Yang, "A survey on transfer learning", IEEE Trans. Knowl. Data Eng., vol. 22, no. 10, pp. 1345-1359, 2010
 +
Abstract | Full Text: PDF (2530KB) | Full Text: HTML
 +
 +
R. Collobert and J. Weston, "A unified architecture for natural language processing: Deep neural networks with multitask learning", Proc. Int. Conf. Mach. Learn., pp. 160-167 [CrossRef]
 +
 +
X. Lu, P. Suryanarayan, R. B. Adams, J. Li, M. G. Newman and J. Z. Wang, "On shape and the computability of emotions", Proc. ACM Int. Conf. Multimedia, pp. 229-238 [CrossRef] 
  
 
== Links ==
 
== Links ==
 
=== Full Text ===  
 
=== Full Text ===  
−
 
+
http://infolab.stanford.edu/~wangz/project/imsearch/Aesthetics/TMM15/lu.pdf
  
 
[[intern file]]
 
[[intern file]]
  
 
=== Sonstige Links ===
 
=== Sonstige Links ===

Version vom 20. Juni 2016, 17:00 Uhr

Reference

Lu, X.; Lin, Z.; Jin, H.; Yang, J.; Wang, J.Z.: Rating Image Aesthetics Using Deep Learning. IEEE Transactions on Multimedia, 2015, Volume: 17, Issue: 11, 2021 - 2034.

DOI

http://dx.doi.org/10.1109/TMM.2015.2477040

Abstract

This paper investigates unified feature learning and classifier training approaches for image aesthetics assessment . Existing methods built upon handcrafted or generic image features and developed machine learning and statistical modeling techniques utilizing training examples. We adopt a novel deep neural network approach to allow unified feature learning and classifier training to estimate image aesthetics. In particular, we develop a double-column deep convolutional neural network to support heterogeneous inputs, i.e., global and local views, in order to capture both global and local characteristics of images . In addition, we employ the style and semantic attributes of images to further boost the aesthetics categorization performance . Experimental results show that our approach produces significantly better results than the earlier reported results on the AVA dataset for both the generic image aesthetics and content -based image aesthetics. Moreover, we introduce a 1.5-million image dataset (IAD) for image aesthetics assessment and we further boost the performance on the AVA test set by training the proposed deep neural networks on the IAD dataset.

Extended Abstract

Bibtex

@ARTICLE{7243357,
author={Lu, X. and Lin, Z. and Jin, H. and Yang, J. and Wang, J.Z.},
journal={Multimedia, IEEE Transactions on},
title={Rating Image Aesthetics Using Deep Learning},
year={2015},
volume={17},
number={11},
pages={2021-2034},
keywords={Computer architecture;Image color analysis;Machine learning;Neural networks;Semantics;Training;Visualization;Automatic feature learning;deep neural networks;image aesthetics},
doi={10.1109/TMM.2015.2477040},
url={http://dx.doi.org/10.1109/TMM.2015.2477040, http://de.evo-art.org/index.php?title=Rating_Image_Aesthetics_Using_Deep_Learning },
ISSN={1520-9210},
month={Nov},
}

Used References

R. Datta, D. Joshi, J. Li and J. Wang, "Studying aesthetics in photographic images using a computational approach", Proc. Eur. Conf. Comput. Vis., pp. 288-301

Y. Ke, X. Tang and F. Jing, "The design of high-level features for photo quality assessment", Proc. IEEE Conf. Comput. Vis. Pattern Recog., vol. 1, pp. 419-426

Y. Luo and X. Tang, "Photo and video quality evaluation: Focusing on the subject", Proc. Eur. Conf. Comput. Vis., pp. 386-399 http://dx.doi.org/10.1007/978-3-540-88690-7_29

S. Bhattacharya, R. Sukthankar and M. Shah, "A framework for photo-quality assessment and enhancement based on visual aesthetics", Proc. ACM Int. Conf. Multimedia, pp. 271-280 [CrossRef]

W. Luo, X. Wang and X. Tang, "Content-based photo quality assessment", Proc. IEEE Int. Conf. Comput. Vis., pp. 2206-2213

S. Dhar, V. Ordonez and T. Berg, "High level describable attributes for predicting aesthetics and interestingness", Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1657-1664 Abstract | Full Text: PDF (4274KB) | Full Text: HTML

M. Nishiyama, T. Okabe, I. Sato and Y. Sato, "Aesthetic quality classification of photographs based on color harmony", Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 33-40 Abstract | Full Text: PDF (2660KB) | Full Text: HTML

P. O¿¿¿Donovan, A. Agarwala and A. Hertzmann, "Color compatibility from large datasets", ACM Trans. Graph., vol. 30, no. 4, pp. 63:1-63:12, 2011 [CrossRef]

L. Marchesotti, F. Perronnin, D. Larlus and G. Csurka, "Assessing the aesthetic quality of photographs using generic image descriptors", Proc. IEEE Int. Conf. Comput. Vis., pp. 1784-1791 Abstract | Full Text: PDF (6768KB) | Full Text: HTML

N. Murray, L. Marchesotti and F. Perronnin, "AVA: A large-scale database for aesthetic visual analysis", Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 2408-2415 Abstract | Full Text: PDF (1112KB) | Full Text: HTML

L. Marchesotti and F. Perronnin, "Learning beautiful (and ugly) attributes", Proc. Brit. Mach. Vis. Conf., pp. 7.1-7.11

D. Lowe, "Distinctive image features from scale-invariant keypoints", Int. J. Comput. Vis., vol. 60, no. 2, pp. 91-110, 2004 [CrossRef]

A. Krizhevsky, I. Sutskever and G. E. Hinton, "Imagenet classification with deep convolutional neural networks", Proc. Adv. Neural Inf. Process. Syst., pp. 1106-1114

H.-H. Su, T.-W. Chen, C.-C. Kao, W. Hsu and S.-Y. Chien, "Scenic photo quality assessment with bag of aesthetics-preserving features", Proc. ACM Int. Conf. Multimedia, pp. 1213-1216 [CrossRef]

A. Oliva and A. Torralba, "Modeling the shape of the scene: A holistic representation of the spatial envelope", Int. J. Comput. Vis., vol. 42, no. 3, pp. 145-175, 2001 [CrossRef]

D. Ciresan, U. Meier and J. Schmidhuber, "Multi-column deep neural networks for image classification", Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3642-3649 Abstract | Full Text: PDF (648KB) | Full Text: HTML

Y. Sun, X. Wang and X. Tang, "Hybrid deep learning for face verification", Proc. IEEE Int. Conf. Comput. Vis., pp. 1489-1496 Abstract | Full Text: PDF (824KB) | Full Text: HTML

P. Sermanet, K. Kavukcuoglu, S. Chintala and Y. LeCun, "Pedestrian detection with unsupervised multi-stage features learning", Proc. IEEE Conf. Computer Vis. Pattern Recog., pp. 3626-3633 Abstract | Full Text: PDF (585KB)

Y. LeCun, L. Bottou, Y. Bengio and P. Haffner, "Gradient-based learning applied to document recognition", Proc. IEEE, vol. 86, no. 11, pp. 2278-2324, 1998 Abstract | Full Text: PDF (896KB)

G. E. Hinton, S. Osindero and Y.-W. Teh, "A fast learning algorithm for deep belief nets", Neural Comput., vol. 18, no. 7, pp. 1527-1554, 2006 [CrossRef]

G. Hinton, "Training products of experts by minimizing contrastive divergence", Neural Comput., vol. 14, no. 8, pp. 1771-1800, 2002 [CrossRef]

S. Karayev, A. Hertzmann, H. Winnermoller, A. Agarwala and T. Darrel, "Recognizing image style", Proc. Brit. Mach. Vis. Conf. [CrossRef]

J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng and T. Darrell, DeCAF: A deep convolutional activation feature for generic visual recognition, 2013

F. Agostinelli, M. Anderson and H. Lee, "Adaptive multi-column deep neural networks with application to robust image denoising", Proc. Adv. Neural Inf. Process. Syst., pp. 1493-1501

A. Khosla, A. Das Sarma and R. Hamid, "What makes an image popular?", Proc. Int. World Wide Web Conf., pp. 867-876 [CrossRef]

O. Litzel, On Photographic Composition, 1974, Amphoto

W. Niekamp, "An exploratory investigation into factors affecting visual balance", Educational Commun. Technol. A, J. Theory, Res., Develop., vol. 29, no. 1, pp. 37-48, 1981

R. Arnheim, Art and Visual Perception: A Psychology of the Creative Eye, 1974, Univ. of California

D. Joshi, R. Datta, E. Fedorovskaya, Q. T. Luong, J. Z. Wang, J. Li and J. B. Luo, "Aesthetics and emotions in images", IEEE Signal Process. Mag., vol. 28, no. 5, pp. 94-115, 2011 Abstract | Full Text: PDF (3585KB) | Full Text: HTML

J. Pan and Q. Yang, "A survey on transfer learning", IEEE Trans. Knowl. Data Eng., vol. 22, no. 10, pp. 1345-1359, 2010 Abstract | Full Text: PDF (2530KB) | Full Text: HTML

R. Collobert and J. Weston, "A unified architecture for natural language processing: Deep neural networks with multitask learning", Proc. Int. Conf. Mach. Learn., pp. 160-167 [CrossRef]

X. Lu, P. Suryanarayan, R. B. Adams, J. Li, M. G. Newman and J. Z. Wang, "On shape and the computability of emotions", Proc. ACM Int. Conf. Multimedia, pp. 229-238 [CrossRef]

Links

Full Text

http://infolab.stanford.edu/~wangz/project/imsearch/Aesthetics/TMM15/lu.pdf

intern file

Sonstige Links

Abgerufen von „http://de.evo-art.org/index.php?title=Rating_Image_Aesthetics_Using_Deep_Learning&oldid=32831“