A Novel Deep CNN Method Based on Aesthetic Rule for User Preferential Images Recommendation

Jing Yu¹ and Lulu Zhao This email address is being protected from spambots. You need JavaScript enabled to view it.¹

¹Luxun Academy of Fine Arts No.19, Sanhao Street, Heping District, Shenyang City

REFERENCES

1. [1] L Teng, H Li, S Yin International Journal of Communication, and undefined 2018. Modified pyramid dual tree direction filter-based image denoising via curvature scale and nonlocal mean multigrade remnant filter. Wiley Online Library, 31(16), nov 2017.
2. [2] Jing Yu, Hang Li, and Shoulin Yin. New intelligent interface study based on K-means gaze tracking. International Journal of Computational Science and Engineering, 18(1):12, 2019.
3. [3] Jing Yu, Hang Li, Shou-Lin Yin, Qingwu Shi, and Shahid Karim. Dynamic Gesture Recognition Based on Deep Learning in Human-to-Computer Interfaces. jase.tku.edu.tw, 23(1):31–38, 2020.
4. [4] Senthil Kumar Sundararajan, B. Sankaragomathi, and D. Saravana Priya. Deep Belief CNN Feature Representation Based Content Based Image Retrieval for Medical Images. Journal of Medical Systems, 43(6), jun 2019.
5. [5] HL Lin Teng, S Yin Journal of Computers, and Undefined 2019. IM-MobiShare: An Improved Privacy Preserving Scheme Based on Asymmetric Encryption and Bloom Filter for Users Location Sharing in Social Network. csroc.org.tw, 3(30):59–71, 2019.
6. [6] Shoulin Yin Jie and Liu Hang Li. A Self-Supervised Learning Method for Shadow Detection in Remote Sensing Imagery. Springer, 9(4), dec 2018.
7. [7] Shoulin Yin, Ye Zhang, and Shahid Karim. Region search based on hybrid convolutional neural network in optical remote sensing images. Trust and Privacy in Blockchain Enabled Internet of Things-Research Article International Journal of Distributed Sensor Networks, 15(5):1–12, may 2019.
8. [8] Peng Li, Zhikui Chen, Laurence Tianruo Yang, Jing Gao, Qingchen Zhang, and M. Jamal Deen. An Incremental Deep Convolutional Computation Model for Feature Learning on Industrial Big Data. IEEE Transactions on Industrial Informatics, 15(3):1341–1349, mar 2019.
9. [9] Peng Li, Zhikui Chen, Laurence Tianruo Yang, Qingchen Zhang, and M. Jamal Deen. Deep convolutional computation model for feature learning on big data in internet of things. IEEE Transactions on Industrial Informatics, 14(2):790–798, feb 2018.
10. [10] Xin Lu, Zhe Lin, Hailin Jin, Jianchao Yang, and James Z. Wang. Rapid: Rating pictorial aesthetics using deep learning. MM 2014 - Proceedings of the 2014 ACM Conference on Multimedia, pages 457–466, nov 2014.
11. [11] Wei-Ning Wang, Li Wang, Ming-Quan Zhao, ChengJia CAI, Ting-Ting SHI, and Xiang-Min XU. Image aesthetic classification using parallel deep convolutional neural networks. Acta Automatica Sinica, 42(6):904–914, 2016.
12. [12] Hanghang Tong, Mingjing Li, Hong Jiang Zhang, Jingrui He, and Changshui Zhang. Classification of digital photos taken by photographers or home users. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3331:198–205, 2004.
13. [13] Ritendra Datta, Dhiraj Joshi, Jia Li, and James Z. Wang. Studying aesthetics in photographic images using a computational approach. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 3953 LNCS, pages 288–301. Springer, Berlin, Heidelberg, 2006.
14. [14] Xiaoou Tang, Wei Luo, and Xiaogang Wang. Contentbased photo quality assessment. IEEE Transactions on Multimedia, 15(8):1930–1943, 2013.
15. [15] Eftichia Mavridaki and Vasileios Mezaris. A comprehensive aesthetic quality assessment method for natural images using basic rules of photography. Proceedings - International Conference on Image Processing, ICIP, 2015- December:887–891, 2015.
16. [16] Sagnik Dhar, Vicente Ordonez, and Tamara L. Berg. High level describable attributes for predicting aesthetics and interestingness. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 1657–1664, 2011.
17. [17] Yuncheng Li, Tao Mei, Yang Cong, and Jiebo Luo. User-curated image collections: Modeling and recommendation. Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015, pages 591–600, 2015.
18. [18] Zhang Chuanyan, Hong Xiaoguang, and Peng Zhaohui. Social image recommendation based on path relevance. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10987 LNCS:165–180, 2018.
19. [19] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. ImageNet Classification with Deep Convolutional Neural Networks. Technical report, 2012.
20. [20] Xinmei Tian, Zhe Dong, Kuiyuan Yang, and Tao Mei. Query-Dependent Aesthetic Model with Deep Learning for Photo Quality Assessment. IEEE Transactions on Multimedia, 17(11):2035–2048, 2015.
21. [21] Yoshiki Ito, Takahiro Ogawa, and Miki Haseyama. Preference estimation for video recommendation using DCNN features and viewing behavior. 2017 IEEE 6th Global Conference on Consumer Electronics, GCCE 2017, 2017-January:1–2, 2017.
22. [22] Jing Zhang, Ying Yang, Qi Tian, Li Zhuo, and Xin Liu. Personalized Social Image Recommendation Method Based on User-Image-Tag Model. IEEE Transactions on Multimedia, 19(11):2439–2449, 2017.
23. [23] Wenhui Yu, Huidi Zhang, Xiangnan He, Xu Chen, Li Xiong, and Zheng Qin. Aesthetic-based clothing recommendation. In The Web Conference 2018 - Proceedings of the World Wide Web Conference, WWW 2018, pages 649– 658, New York, New York, USA, apr 2018. Association for Computing Machinery, Inc.
24. [24] Hanh T.H. Nguyen, Martin Wistuba, and Lars Schmidt-Thieme. Personalized Tag Recommendation for Images Using Deep Transfer Learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 10535 LNAI, pages 705–720. Springer Verlag, 2017.