Object semantics sentiment correlation analysis enhanced image sentiment classification

Joshi, 2011, Aesthetics and emotions in images, IEEE Signal Process. Mag., 28, 94, 10.1109/MSP.2011.941851 X. Lu, P. Suryanarayan, R.B. Adams, Jr., J. Li, M.G. Newman, J.Z. Wang, On shape and the computability of emotions, in: Proceedings of the 20th ACM Multimedia Conference, MM ’12, Nara, Japan, October 29 - November 02, 2012, 2012, pp. 229–238. Y. Yang, J. Jia, S. Zhang, B. Wu, Q. Chen, J. Li, C. Xing, J. Tang, How do your friends on social media disclose your emotions? in: Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, July 27 -31, 2014, QuéBec City, QuéBec, Canada, 2014, pp. 306–312. Wang, 2015, Multiple emotion tagging for multimedia data by exploiting high-order dependencies among emotions, IEEE Trans. Multimedia, 17, 2185, 10.1109/TMM.2015.2484966 Borth, 2013, Sentibank: large-scale ontology and classifiers for detecting sentiment and emotions in visual content, 459 J. Yuan, S. Mcdonough, Q. You, J. Luo, Sentribute: image sentiment analysis from a mid-level perspective, in: Proceedings of the Second International Workshop on Issues of Sentiment Discovery and Opinion Mining, WISDOM 2013, Chicago, IL, USA, August 11, 2013, 2013, pp. 10:1–10:8. Zhao, 2017, Continuous probability distribution prediction of image emotions via multitask shared sparse regression, IEEE Trans. Multimedia, 19, 632, 10.1109/TMM.2016.2617741 V. Campos, A. Salvador, X. Giró i Nieto, B. Jou, Diving deep into sentiment: Understanding fine-tuned cnns for visual sentiment prediction, in: Proceedings of the 1st International Workshop on Affect & Sentiment in Multimedia, ASM 2015, Brisbane, Australia, October 30, 2015, 2015, pp. 57–62. Q. You, J. Luo, H. Jin, J. Yang, Robust image sentiment analysis using progressively trained and domain transferred deep networks, in: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, January 25-30, 2015, Austin, Texas, USA, 2015, pp. 381–388. J. Wang, J. Fu, Y. Xu, T. Mei, Beyond object recognition: Visual sentiment analysis with deep coupled adjective and noun neural networks, in: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI 2016, New York, NY, USA, 9-15 July 2016, 2016, pp. 3484–3490. Rao, 2019, Multi-level region-based convolutional neural network for image emotion classification, Neurocomputing, 333, 429, 10.1016/j.neucom.2018.12.053 Yang, 2018, Visual sentiment prediction based on automatic discovery of affective regions, IEEE Trans. Multimedia, 20, 2513, 10.1109/TMM.2018.2803520 J. Machajdik, A. Hanbury, Affective image classification using features inspired by psychology and art theory, in: Proceedings of the 18th International Conference on Multimedia 2010, Firenze, Italy, October 25-29, 2010, 2010, pp. 83–92. A. Sartori, D. Culibrk, Y. Yan, N. Sebe, Who’s afraid of itten: Using the art theory of color combination to analyze emotions in abstract paintings, in: Proceedings of the 23rd Annual ACM Conference on Multimedia Conference, MM ’15, Brisbane, Australia, October 26 - 30, 2015, 2015, pp. 311–320. S. Zhao, Y. Gao, X. Jiang, H. Yao, T. Chua, X. Sun, Exploring principles-of-art features for image emotion recognition, in: Proceedings of the ACM International Conference on Multimedia, MM ’14, Orlando, FL, USA, November 03 - 07, 2014, 2014, pp. 47–56. V. Yanulevskaya, J.C. van Gemert, K. Roth, A. Herbold, N. Sebe, J. Geusebroek, Emotional valence categorization using holistic image features, in: Proceedings of the International Conference on Image Processing, ICIP 2008, October 12-15, 2008, San Diego, California, USA, 2008, pp. 101–104. Borth, 2013, Large-scale visual sentiment ontology and detectors using adjective noun pairs, 223 Li, 2018, Image sentiment prediction based on textual descriptions with adjective noun pairs, Multimedia Tools Appl., 77, 1115, 10.1007/s11042-016-4310-5 Krizhevsky, 2017, Imagenet classification with deep convolutional neural networks, Commun. ACM, 60, 84, 10.1145/3065386 Long, 2015, Fully convolutional networks for semantic segmentation, 3431 Ren, 2015, Faster R-CNN: towards real-time object detection with region proposal networks, 91 Zhou, 2014, Learning deep features for scene recognition using places database, 487 Peng, 2015, A mixed bag of emotions: Model, predict, and transfer emotion distributions, 860 Q. You, J. Luo, H. Jin, J. Yang, Building a large scale dataset for image emotion recognition: The fine print and the benchmark, in: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, February 12-17, 2016, Phoenix, Arizona, USA, 2016, pp. 308–314. Rao, 2016 X. Zhu, L. Li, W. Zhang, T. Rao, M. Xu, Q. Huang, D. Xu, Dependency exploitation: A unified CNN-RNN approach for visual emotion recognition, in: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, August 19-25, 2017, 2017, pp. 3595–3601. Islam, 2016, Visual sentiment analysis for social images using transfer learning approach, 124 Song, 2018, Boosting image sentiment analysis with visual attention, Neurocomputing, 312, 218, 10.1016/j.neucom.2018.05.104 Yang, 2018, Weakly supervised coupled networks for visual sentiment analysis, 7584 Y. Wang, Y. Hu, S. Kambhampati, B. Li, Inferring sentiment from web images with joint inference on visual and social cues: A regulated matrix factorization approach, in: Proceedings of the Ninth International Conference on Web and Social Media, ICWSM 2015, University of Oxford, Oxford, UK, May 26-29, 2015, 2015, pp. 473–482. Chen, 2017, Visual and textual sentiment analysis using deep fusion convolutional neural networks, 1557 Katsurai, 2016, Image sentiment analysis using latent correlations among visual, textual, and sentiment views, 2837 A. Esuli, F. Sebastiani, SENTIWORDNET: A publicly available lexical resource for opinion mining, in: Proceedings of the Fifth International Conference on Language Resources and Evaluation, LREC 2006, Genoa, Italy, May 22-28, 2006, 2006, pp. 417–422. Simonyan, 2015, Very deep convolutional networks for large-scale image recognition, 1 Wang, 2018, Novel binary encoding water cycle algorithm for solving bayesian network structures learning problem, Knowl.-Based Syst., 150, 95, 10.1016/j.knosys.2018.03.007 Madsen, 2017, A parallel algorithm for bayesian network structure learning from large data sets, Knowl.-Based Syst., 117, 46, 10.1016/j.knosys.2016.07.031 Liu, 2017, A new hybrid method for learning bayesian networks: Separation and reunion, Knowl.-Based Syst., 121, 185, 10.1016/j.knosys.2017.01.029 Li, 2017, A safe control scheme under the abnormity for the thickening process of gold hydrometallurgy based on bayesian network, Knowl.-Based Syst., 119, 10, 10.1016/j.knosys.2016.11.026 Abolbashari, 2018, Smart buyer: A bayesian network modelling approach for measuring and improving procurement performance in organisations, Knowl.-Based Syst., 142, 127, 10.1016/j.knosys.2017.11.032 Arias, 2017, Learning distributed discrete bayesian network classifiers under mapreduce with apache spark, Knowl.-Based Syst., 117, 16, 10.1016/j.knosys.2016.06.013 Varshney, 2017, Predicting information diffusion probabilities in social networks: A bayesian networks based approach, Knowl.-Based Syst., 133, 66, 10.1016/j.knosys.2017.07.003 Moskopp, 2019, Bayesian inference for the automated adjustment of an image segmentation pipeline - A modular approach applied to wound healing assays, Knowl.-Based Syst., 173, 52, 10.1016/j.knosys.2019.02.025 Gross, 2019, An analytical threshold for combining bayesian networks, Knowl.-Based Syst., 175, 36, 10.1016/j.knosys.2019.03.014 How to use the bayes net toolbox, https://www.cs.ubc.ca/ murphyk/Software/BNT/usage.html. Cooper, 1992, A bayesian method for the induction of probabilistic networks from data, Mach. Learn., 9, 309, 10.1007/BF00994110 Waltman, 2005, Maximum likelihood parameter estimation in probabilistic fuzzy classifiers, 1098 Ren, 2017, Faster R-CNN: towards real-time object detection with region proposal networks, IEEE Trans. Pattern Anal. Mach. Intell., 39, 1137, 10.1109/TPAMI.2016.2577031 J. Yang, M. Sun, X. Sun, Learning visual sentiment distributions via augmented conditional probability neural network, in: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, February 4-9, 2017, San Francisco, California, USA, 2017, pp. 224–230. PyTorch, https://pytorch.org/. Rao, 2016, Multi-scale blocks based image emotion classification using multiple instance learning, 634 He, 2016, Deep residual learning for image recognition, 770 Rao, 2016 Gao, 2017, Deep label distribution learning with label ambiguity, IEEE Trans. Image Process., 26, 2825, 10.1109/TIP.2017.2689998 J. Yang, D. She, M. Sun, Joint image emotion classification and distribution learning via deep convolutional neural network, in: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, August 19-25, 2017, 2017, pp. 3266–3272.