Automatic aesthetic quality assessment of photographic images using deep convolutional neural network

Aesthetics can be defined as the study of various emotions related to the sense of beauty. Nowadays there is a fast growth in the use of digital images which is used for representing information. With the increase in the number of photos, it has become complicated to evaluate its quality. Image aesthetic evaluation methods depends on many aesthetic features. People give more attention to the photos which are visually satisfying. Evaluation of aesthetic quality is a challenging task which require knowledge about aesthetic features. Some low level features like contrast, sharpness, colorfulness etc are used to calculate aesthetic score of an image. This paper present a system for automatic aesthetic quality assessment of photographic images using deep convolutional neural network.

[1]  Nuria Oliver,et al.  Towards Computational Models of the Visual Aesthetic Appeal of Consumer Videos , 2010, ECCV.

[2]  James Ze Wang,et al.  Studying Aesthetics in Photographic Images Using a Computational Approach , 2006, ECCV.

[3]  Rongrong Ji,et al.  Photo assessment based on computational visual attention model , 2009, ACM Multimedia.

[4]  ON-SITE COMPOSITION AND AESTHETCS FEEDBACK THROUGH EXEMPLARS FOR PHOTOGRAPHERS , 2017 .

[5]  Vicente Ordonez,et al.  High level describable attributes for predicting aesthetics and interestingness , 2011, CVPR 2011.

[6]  James Ze Wang,et al.  ACQUINE: aesthetic quality inference engine - real-time automatic rating of photo aesthetics , 2010, MIR '10.

[7]  Aaron Hertzmann,et al.  Color compatibility from large datasets , 2011, SIGGRAPH 2011.

[8]  Gabriela Csurka,et al.  Assessing the aesthetic quality of photographs using generic image descriptors , 2011, 2011 International Conference on Computer Vision.

[9]  Ming Ouhyoung,et al.  Personalized photograph ranking and selection system , 2010, ACM Multimedia.

[10]  Daniel Cohen-Or,et al.  Color harmonization , 2006, ACM Trans. Graph..

[11]  James Ze Wang,et al.  Algorithmic inferencing of aesthetics and emotion in natural images: An exposition , 2008, 2008 15th IEEE International Conference on Image Processing.

[12]  Tsuhan Chen,et al.  > Replace This Line with Your Paper Identification Number (double-click Here to Edit) < , 2022 .

[13]  Yan Ke,et al.  The Design of High-Level Features for Photo Quality Assessment , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[14]  Naila Murray,et al.  AVA: A large-scale database for aesthetic visual analysis , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  Xiaoou Tang,et al.  Photo and Video Quality Evaluation: Focusing on the Subject , 2008, ECCV.

[16]  Jiebo Luo,et al.  Aesthetics and Emotions in Images , 2011, IEEE Signal Processing Magazine.

[17]  Mu Qiao,et al.  OSCAR: On-Site Composition and Aesthetics Feedback Through Exemplars for Photographers , 2012, International Journal of Computer Vision.

[18]  Daniel Cohen-Or,et al.  Optimizing Photo Composition , 2010, Comput. Graph. Forum.

[19]  Andreas E. Savakis,et al.  Evaluation of image appeal in consumer photography , 2000, Electronic Imaging.

[20]  Shao-Yi Chien,et al.  Preference-Aware View Recommendation System for Scenic Photos Based on Bag-of-Aesthetics-Preserving Features , 2012, IEEE Transactions on Multimedia.

[21]  Brian Curless,et al.  Candid portrait selection from video , 2011, ACM Trans. Graph..

[22]  Tsuhan Chen,et al.  Towards aesthetics: a photo quality assessment and photo selection system , 2010, ACM Multimedia.

[23]  Yong-Jin Liu,et al.  Image Retargeting Quality Assessment , 2011, Comput. Graph. Forum.

[24]  Mubarak Shah,et al.  A framework for photo-quality assessment and enhancement based on visual aesthetics , 2010, ACM Multimedia.

[25]  Masashi Nishiyama,et al.  Aesthetic quality classification of photographs based on color harmony , 2011, CVPR 2011.

[26]  Ligang Liu,et al.  Realtime Aesthetic Image Retargeting , 2010, CAe.