Mood Detection through Aesthetic Assessment of Videos using Deep Learning
role. Monitoring and predicting various
human’s feelings (happy, sad, anger, fear, etc.) is a challenging
task. Human interaction and carrier of feelings amongst
humans are accomplished mainly through five senses: touch,
smell, taste audio, visual. Considering Visual sense, images and
videos are important gradients in day-to-day life. It can
elevate/ depress the mood of a person. Digital contents of
multimedia are image, audio, video, text, and so on. The usage
of internet is tremendously increasing, so Internet bandwidth
and storage space, video data has been generated, published,
and spread robustly, and becoming an important of today’s big
data. This has encouraged the development of advanced
techniques for a wide scope of video understanding
applications including online advertising, Cinematography,
video retrieval, video surveillance, video data on Social sites,
etc. However, it is easy to convey a story to a viewer of video,
since a video is worth of thousands worth. And this story
actually creates a mood. This work is to detect the mood of
aesthetically pleasing videos that reflect on a person’s mood.
Huang, Chengjia Cai, Xiangmin Xu, “A multi-scene
deep learning model for image aesthetic evaluation”,
2016, ACM journal Image communication, Vol.47, Issue
C, Pages 511-518.
 Magzhan Kairanbay, John See, Lai-Kuan Wong, Yong-
Lian Hii, “Filling the GAPS: Reducing the Complexity of
Networks for Multi-Attribute Image Aesthetic
Prediction.” 2017 IEEE International Conference on
 Simone Bianco, Luigi Celona, Paolo Napoletano, and
Raimondo Schettini, “Predicting Image Aesthetics with
Deep Learning”, 2018 Springer international Journal.
 Michal Kucer, Alexander C. Loui, David W. Messinger,
“Leveraging Expert Feature Knowledge for Predicting
Image Aesthetics”, 2018 IEEE Transactions on Image
Processing, Vol. 27, No. 10.
 Zhangyang Wang, Ding Liu, Shiyu Chang, Florin Dolcos,
Diane Beck, Thomas Huang, “Image Aesthetics
Assessment using Deep Chatterjee’s Machine” 2017
IEEE International Joint Conference on Neural
 Xin Lu, Zhe Lin, Hailin Jin, Jianchao Yang, and James. Z.
Wang, “Rating Image Aesthetics Using Deep Learning”,
2015 IEEE Transactions on Multimedia, Vol. 17, No. 11.
 Hui-Jin Lee, Ki-Sang Hong, Henry Kang, and Seungyong
Lee, “Photo Aesthetics Analysis via DCNN Feature
19, No. 8.
 Luming Zhangy, Yue Gaoy, Chao Zhangz, Hanwang
Zhangy, Qi Tian, Roger Zimmermann, “Perception-
Guided Multimodal Feature Fusion for Photo Aesthetics
Assessment”, 2014, Proceeding of the 22nd ACM
international conference on Multimedia Pages 237-246.
 Yuzhen Niu and Feng Liu, “What Makes a Professional
Video? A Computational Aesthetics Approach”, July
2012 IEEE Transactions on Circuits and Systems for
Video Technology, Vol. 22, issue:7
 Yanran Wang, Qi Dai, Rui Feng, Yu-Gang Jiang, “Beauty
is Here: Evaluating Aesthetics in Videos Using
Multimodal Features and Free Training Data”, 2013,
Proceeding of the 21st ACM international conference on
Multimedia Pages 369-372.
 Chun-Yu Yang, Hsin-Ho Yeh and Chu-Song Chen,
“Video Aesthetic quality assessment by combining
semantically independent and dependent features”, 2011
IEEE International conference on Acoustics, Speech, and
 Hsin-Ho Yeh, Chun-Yu Yang, Ming-Sui Lee, and Chu-
Song Chen, “Video Aesthetic Quality Assessment by
Temporal Integration of Photo- and Motion-Based
Features”, Dec 2013 IEEE Transactions on Multimedia,
Vol. 15, issue: 8
 Subhabrata Bhattacharya, Behnaz Nojavanasghari, Tao
Chen, “Towards a Comprehensive Computational Model
for Aesthetic Assessment of Videos”, 2013, Proceeding of
the 21st ACM international conference on Multimedia
 Shasha Moa, Jianwei Niua, Yiming Sua, Sajal K. Das, “A
Novel Feature Set for Video Emotion Recognition”, 2018
Neurocomputing 291, 11-20.
 Christos Tzelepis, Eftichia Mavridaki, Vasileios Mezaris,
Ioannis Patras, “Video Aesthetic Quality Assessment
using Kernel Support Vector Machine with Isotropic
Gaussian Sample Uncertainty (KSVM-IGSU)”, 2016
IEEE International Conference on Image Processing
 Loris Nanni, Stefano Ghidoni, Sheryl Brahnam,
“Handcrafted vs Non-Handcrafted Features for computer
vision classification”, 2017 Pattern Recognition 71, 158-
 Sanjay K. Kuanar, Rameswar Panda, Ananda S.
Chowdhury, “Video key frame extraction through
dynamic Delaunay clustering with a structural
constraint”, 2013 journal of Visual Communication and
Image Representation, Vol. 24, issue: 7, Pages 1212-
 Ritendra Datta, James Z. Wang, “ACQUINE: Aesthetic
Quality Inference Engine –Real-time Automatic Rating
of Photo Aesthetics”, Proceedings of the ACM
international conference on Multimedia information
retrieval Pages 421-424.
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