11.2.1.2.7 Visual Sentiment Evaluation

Chapter Contents (Back)
Visual Sentiment.

Neviarouskaya, A., Prendinger, H., Ishizuka, M.,
SentiFul: A Lexicon for Sentiment Analysis,
AffCom(2), No. 1, 2011, pp. 22-36.
IEEE DOI 1202
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Clavel, C., Callejas, Z.,
Sentiment Analysis: From Opinion Mining to Human-Agent Interaction,
AffCom(7), No. 1, January 2016, pp. 74-93.
IEEE DOI 1603
Analytical models BibRef

Zadeh, A., Zellers, R., Pincus, E., Morency, L.P.[Louis-Philippe],
Multimodal Sentiment Intensity Analysis in Videos: Facial Gestures and Verbal Messages,
IEEE_Int_Sys(31), No. 6, November 2016, pp. 82-88.
IEEE DOI 1612
Feature extraction BibRef

Choi, Y., Wiebe, J., Mihalcea, R.,
Coarse-Grained +/-Effect Word Sense Disambiguation for Implicit Sentiment Analysis,
AffCom(8), No. 4, October 2017, pp. 471-479.
IEEE DOI 1712
Bridges, Gold, Knowledge based systems, Medical services, Sentiment analysis, Standards, Training data, Sentiment analysis, word sense disambiguation BibRef

Dragoni, M., Petrucci, G.,
A Neural Word Embeddings Approach for Multi-Domain Sentiment Analysis,
AffCom(8), No. 4, October 2017, pp. 457-470.
IEEE DOI 1712
Analytical models, Buildings, Computer architecture, Context, Machine learning, Sentiment analysis, Social network services, neural networks BibRef

Soleymani, M.[Mohammad], Garcia, D.[David], Jou, B.[Brendan], Schuller, B.[Björn], Chang, S.F.[Shih-Fu], Pantic, M.[Maja],
A survey of multimodal sentiment analysis,
IVC(65), No. 1, 2017, pp. 3-14.
Elsevier DOI 1709
Sentiment BibRef

Campos, V.[Víctor], Jou, B.[Brendan], Giró-i-Nieto, X.[Xavier],
From pixels to sentiment: Fine-tuning CNNs for visual sentiment prediction,
IVC(65), No. 1, 2017, pp. 15-22.
Elsevier DOI 1709
Sentiment BibRef

Sharma, S.[Srishti], Chakraverty, S.[Shampa], Sharma, A.[Akhil], Kaur, J.[Jasleen],
A context-based algorithm for sentiment analysis,
IJCVR(7), No. 5, 2017, pp. 558-573.
DOI Link 1709
BibRef

Weichselbraun, A., Gindl, S., Fischer, F., Vakulenko, S., Scharl, A.,
Aspect-Based Extraction and Analysis of Affective Knowledge from Social Media Streams,
IEEE_Int_Sys(32), No. 3, May 2017, pp. 80-88.
IEEE DOI 1706
Automobiles, Companies, Data mining, Knowledge acquisition, Media, Sentiment analysis, Social network services, affective knowledge extraction, artificial intelligence, aspect-based sentiment analysis, linked data, opinion targets, social, media BibRef

Pappas, N.[Nikolaos], Redi, M.[Miriam], Topkara, M.[Mercan], Liu, H.Y.[Hong-Yi], Jou, B.[Brendan], Chen, T.[Tao], Chang, S.F.[Shih-Fu],
Multilingual visual sentiment concept clustering and analysis,
MultInfoRetr(6), No. 1, March 2017, pp. 51-70.
Springer DOI 1704
BibRef

Dragoni, M., Poria, S., Cambria, E.,
OntoSenticNet: A Commonsense Ontology for Sentiment Analysis,
IEEE_Int_Sys(33), No. 3, May 2018, pp. 77-85.
IEEE DOI 1808
Ontologies, Sentiment analysis, Semantics, Task analysis, Affective computing, Feature extraction, sentiment analysis, artificial intelligence BibRef

Liu, A.[Anan], Shi, Y.D.[Ying-Di], Jing, P.G.[Pei-Guang], Liu, J.[Jing], Su, Y.T.[Yu-Ting],
Low-rank regularized multi-view inverse-covariance estimation for visual sentiment distribution prediction,
JVCIR(57), 2018, pp. 243-252.
Elsevier DOI 1812
Using images to express opinions and share experiences. Image sentiment, Label distribution learning, Structured sparsity, Low-rank BibRef

Yang, J., She, D., Sun, M., Cheng, M., Rosin, P.L., Wang, L.,
Visual Sentiment Prediction Based on Automatic Discovery of Affective Regions,
MultMed(20), No. 9, September 2018, pp. 2513-2525.
IEEE DOI 1809
image representation, learning (artificial intelligence), neural nets, object tracking, automatic discovery, visual sentiment analysis BibRef

Ding, X., Chen, Z.,
Improving Saliency Detection Based on Modeling Photographer's Intention,
MultMed(21), No. 1, January 2019, pp. 124-134.
IEEE DOI 1901
Saliency detection, Visualization, Psychology, Image color analysis, Task analysis, Feature extraction, intention rate BibRef

Liu, X.[Xuan], Li, N.[Na], Xia, Y.[Yong],
Affective image classification by jointly using interpretable art features and semantic annotations,
JVCIR(58), 2019, pp. 576-588.
Elsevier DOI 1901
Affective image classification, Discrete emotion space, Deep convolutional neural network (DCNN), Feature extraction, Support vector machine (SVM) BibRef

Jin, X.[Xin], Wu, L.[Le], Li, X.D.[Xiao-Dong], Zhang, X.K.[Xiao-Kun], Chi, J.Y.[Jing-Ying], Peng, S.W.[Si-Wei], Ge, S.M.[Shi-Ming], Zhao, G.[Geng], Li, S.Y.[Shu-Ying],
ILGNet: inception modules with connected local and global features for efficient image aesthetic quality classification using domain adaptation,
IET-CV(13), No. 2, March 2019, pp. 206-212.
DOI Link 1902
BibRef

Pluta, M.[Magda], Mitka, B.[Bartosz],
V-Factor Indicator in the Assessment of the Change in the Attractiveness of View as a Result of the Implementation of a Specific Planning Scenario,
IJGI(8), No. 2, 2019, pp. xx-yy.
DOI Link 1903
BibRef

Zhang, X., Li, Z., Constable, M., Chan, K.L., Tang, Z., Tang, G.,
Pose-Based Composition Improvement for Portrait Photographs,
CirSysVideo(29), No. 3, March 2019, pp. 653-668.
IEEE DOI 1903
Painting, Face, Pose estimation, Training, Databases, Electronic mail, Area measurement, Composition improvement, pose, portrait BibRef

Cui, C., Liu, H., Lian, T., Nie, L., Zhu, L., Yin, Y.,
Distribution-Oriented Aesthetics Assessment With Semantic-Aware Hybrid Network,
MultMed(21), No. 5, May 2019, pp. 1209-1220.
IEEE DOI 1905
computer vision, convolutional neural nets, image representation, object recognition, image aesthetics assessment, semantic fusion BibRef

Wang, M.[Meili], Guo, S.[Shihui], Liao, M.H.[Ming-Hong], He, D.J.[Dong-Jian], Chang, J.[Jian], Zhang, J.J.[Jian-Jun],
Action snapshot with single pose and viewpoint,
VC(35), No. 4, April 2019, pp. 507-520.
Springer DOI 1906
Select a meaningful representative moment from an action performance. BibRef

Zhang, W.J.[Wen-Jie], Yao, Y.Y.[Yi-Yang], Wang, J.X.[Jin-Xiong], Xiang, X.Y.[Xin-Yu], Shu, P.[Peng],
Image quality tendency modeling by fusing multiple visual cues,
JVCIR(62), 2019, pp. 117-128.
Elsevier DOI 1908
Machine learning, Multi-cue fusion, Aesthetic tendency, Flickr, Graph mining BibRef

Zeng, H., Cao, Z., Zhang, L., Bovik, A.C.,
A Unified Probabilistic Formulation of Image Aesthetic Assessment,
IP(29), No. , 2020, pp. 1548-1561.
IEEE DOI 1911
Task analysis, Probabilistic logic, Computational modeling, Measurement, Predictive models, Training, Explosives, unified probabilistic formulation BibRef

Zhang, X., Gao, X., Lu, W., He, L.,
A Gated Peripheral-Foveal Convolutional Neural Network for Unified Image Aesthetic Prediction,
MultMed(21), No. 11, November 2019, pp. 2815-2826.
IEEE DOI 1911
Feature extraction, Logic gates, Visualization, Convolutional neural networks, Task analysis, Deep learning, deep learning BibRef


Ye, J.[Jin], Peng, X.J.[Xiao-Jiang], Qiao, Y.[Yu], Xing, H.[Hao], Li, J.L.[Jun-Li], Ji, R.R.[Rong-Rong],
Visual-Textual Sentiment Analysis in Product Reviews,
ICIP19(869-873)
IEEE DOI 1910
sentiment analysis, product reviews, tucker decomposition, DTF BibRef

Wang, W.N.[Wei-Ning], Su, J.[Junjie], Li, L.[Lemin], Xu, X.M.[Xiang-Min], Luo, J.[Jiebo],
Meta-Learning Perspective for Personalized Image Aesthetics Assessment,
ICIP19(1875-1879)
IEEE DOI 1910
Image Aesthetics, Personalized Preference, Meta-Learning, Meta regularization, Deep Learning BibRef

Zhang, W., Zhai, G., Yang, X., Yan, J.,
Hierarchical Features Fusion for Image Aesthetics Assessment,
ICIP19(3771-3775)
IEEE DOI 1910
Image Aesthetics Assessment, Low-rank Bilinear Pooling, Hierarchical Features BibRef

Yu, J., Cui, C., Geng, L., Ma, Y., Yin, Y.,
Towards Unified Aesthetics and Emotion Prediction in Images,
ICIP19(2526-2530)
IEEE DOI 1910
Aesthetics assessment, emotion recognition, multi-task learning BibRef

Wang, W., Deng, R.,
Modeling Human Perception for Image Aesthetic Assessment,
ICIP19(1029-1033)
IEEE DOI 1910
Image Aesthetic Assessment, Deep Neural Network, Attractive Region, Adaptive Aggregation BibRef

Rodríguez-Pardo, C.[Carlos], Bilen, H.[Hakan],
Personalised Aesthetics with Residual Adapters,
IbPRIA19(I:508-520).
Springer DOI 1910
BibRef

Felicetti, A.[Andrea], Martini, M.[Massimo], Paolanti, M.[Marina], Pierdicca, R.[Roberto], Frontoni, E.[Emanuele], Zingaretti, P.[Primo],
Visual and Textual Sentiment Analysis of Daily News Social Media Images by Deep Learning,
CIAP19(I:477-487).
Springer DOI 1909
BibRef

Ortis, A.[Alessandro], Farinella, G.M.[Giovanni Maria], Battiato, S.[Sebastiano],
Prediction of Social Image Popularity Dynamics,
CIAP19(II:572-582).
Springer DOI 1909
BibRef

Stefanini, M.[Matteo], Cornia, M.[Marcella], Baraldi, L.[Lorenzo], Corsini, M.[Massimiliano], Cucchiara, R.[Rita],
Artpedia: A New Visual-Semantic Dataset with Visual and Contextual Sentences in the Artistic Domain,
CIAP19(II:729-740).
Springer DOI 1909
BibRef

Offert, F.[Fabian],
Images of Image Machines. Visual Interpretability in Computer Vision for Art,
CVAA18(II:710-715).
Springer DOI 1905
BibRef

Garcia, N.[Noa], Vogiatzis, G.[George],
How to Read Paintings: Semantic Art Understanding with Multi-modal Retrieval,
CVAA18(II:676-691).
Springer DOI 1905
BibRef

Gonthier, N.[Nicolas], Gousseau, Y.[Yann], Ladjal, S.[Said], Bonfait, O.[Olivier],
Weakly Supervised Object Detection in Artworks,
CVAA18(II:692-709).
Springer DOI 1905
BibRef

Cianci, M.G., Molinari, M.,
Information Modeling and Landscape: Intervention Methodology For Reading Complex Systems,
3DARCH19(269-276).
DOI Link 1904
Aesthetics of landscapes. BibRef

Ma, N., Volkov, A., Livshits, A., Pietrusinski, P., Hu, H., Bolin, M.,
An Universal Image Attractiveness Ranking Framework,
WACV19(657-665)
IEEE DOI 1904
image classification, image retrieval, indexing, learning (artificial intelligence), neural nets, search engines, Indexes BibRef

Saito, J.[Junki], Nakamura, S.[Satoshi],
Fontender: Interactive Japanese Text Design with Dynamic Font Fusion Method for Comics,
MMMod19(II:554-559).
Springer DOI 1901
BibRef

Apostolidis, K.[Konstantinos], Mezaris, V.[Vasileios],
Image Aesthetics Assessment Using Fully Convolutional Neural Networks,
MMMod19(I:361-373).
Springer DOI 1901
BibRef

Wei, Z., Zhang, J., Shen, X., Lin, Z., Mech, R., Hoai, M., Samaras, D.,
Good View Hunting: Learning Photo Composition from Dense View Pairs,
CVPR18(5437-5446)
IEEE DOI 1812
Proposals, Training, Agriculture, Task analysis, Knowledge transfer, Protocols, Virtual private networks BibRef

Liu, W., Fu, X.,
Introduce More Characteristics of Samples into Cross-domain Sentiment Classification,
ICPR18(25-30)
IEEE DOI 1812
Training, Adaptation models, Neural networks, Task analysis, Training data, Mathematical model, Data models BibRef

Yang, J., She, D., Lai, Y., Rosin, P.L., Yang, M.,
Weakly Supervised Coupled Networks for Visual Sentiment Analysis,
CVPR18(7584-7592)
IEEE DOI 1812
Visualization, Proposals, Feature extraction, Sentiment analysis, Task analysis, Training, Twitter BibRef

Fan, S., Shen, Z., Jiang, M., Koenig, B.L., Xu, J., Kankanhalli, M.S., Zhao, Q.,
Emotional Attention: A Study of Image Sentiment and Visual Attention,
CVPR18(7521-7531)
IEEE DOI 1812
Semantics, Visualization, Computational modeling, Observers, Neural networks, Benchmark testing, Image annotation BibRef

Paolanti, M.[Marina], Kaiser, C.[Carolin], Schallner, R.[René], Frontoni, E.[Emanuele], Zingaretti, P.[Primo],
Visual and Textual Sentiment Analysis of Brand-Related Social Media Pictures Using Deep Convolutional Neural Networks,
CIAP17(I:402-413).
Springer DOI 1711
BibRef

Ullah, M.A., Islam, M.M., Azman, N.B., Zaki, Z.M.,
An overview of Multimodal Sentiment Analysis research: Opportunities and Difficulties,
IVPR17(1-6)
IEEE DOI 1704
Face BibRef

Nemati, S., Naghsh-Nilchi, A.R.,
Exploiting evidential theory in the fusion of textual, audio, and visual modalities for affective music video retrieval,
IPRIA17(222-228)
IEEE DOI 1712
emotion recognition, image fusion, inference mechanisms, sentiment analysis, social networking (online), Lexicon-based sentiment analysis BibRef

Wu, L., Liu, S., Jian, M., Luo, J., Zhang, X., Qi, M.,
Reducing noisy labels in weakly labeled data for visual sentiment analysis,
ICIP17(1322-1326)
IEEE DOI 1803
Indexes, Visual sentiment analysis, deep learning, mislabeled images, sentiment conflict BibRef

Chen, X., Wang, Y., Liu, Q.,
Visual and textual sentiment analysis using deep fusion convolutional neural networks,
ICIP17(1557-1561)
IEEE DOI 1803
Convolutional neural networks, Feature extraction, Semantics, Sentiment analysis, Social network services, Training, visual sentiment BibRef

Zheng, H., Chen, T., You, Q., Luo, J.,
When saliency meets sentiment: Understanding how image content invokes emotion and sentiment,
ICIP17(630-634)
IEEE DOI 1803
Analytical models, Computational modeling, Correlation, Proposals, Saliency detection, Sentiment analysis, Visualization, saliency, sentiment perception BibRef

Niu, T.[Teng], Zhu, S.[Shiai], Pang, L.[Lei], El Saddik, A.[Abdulmotaleb],
Sentiment Analysis on Multi-View Social Data,
MMMod16(II: 15-27).
Springer DOI 1601
BibRef

Shin, A.[Andrew], Ushiku, Y.[Yoshitaka], Harada, T.[Tatsuya],
Image Captioning with Sentiment Terms via Weakly-Supervised Sentiment Dataset,
BMVC16(xx-yy).
HTML Version. 1805
BibRef

Chapter on 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing continues in
Rendering Specific Surfaces, Applied Rendering .


Last update:Dec 7, 2019 at 17:16:29