11.2.1.2.6 Aesthetic Quality, Aesthetic Evaluation

Chapter Contents (Back)
Aesthetic Quality. See also Lighting Effects, View Generation, Graphics Issues. See also Graphics, Rendering Issues Related to Artistic Interpretation. See also Color Transfer, Color Enhancement, Color Correction.

Shin, Y.H.[Yun-Hee], Kim, Y.R.[Young-Rae], Kim, E.Y.[Eun Yi],
Automatic textile image annotation by predicting emotional concepts from visual features,
IVC(28), No. 3, March 2010, pp. 526-537.
Elsevier DOI 1001
Automatic image annotation; Affective features; Emotion recognition; Textile image retrieval: neural network BibRef

Joshi, D., Datta, R., Fedorovskaya, E., Luong, Q.T.[Quang-Tuan], Wang, J.Z., Li, J.[Jia], Luo, J.B.[Jie-Bo],
Aesthetics and Emotions in Images,
SPMag(28), No. 5, 2011, pp. 94-115.
IEEE DOI 1109
BibRef

Niu, Y.[Yuzhen], Liu, F.[Feng],
What Makes a Professional Video? A Computational Aesthetics Approach,
CirSysVideo(22), No. 7, July 2012, pp. 1037-1049.
IEEE DOI 1208
BibRef

Zhang, F.L.[Fang-Lue], Wang, M.[Miao], Hu, S.M.[Shi-Min],
Aesthetic Image Enhancement by Dependence-Aware Object Recomposition,
MultMed(15), No. 7, 2013, pp. 1480-1490.
IEEE DOI 1312
heuristic programming. Optimize photograph composition by rearranging foreground objects. BibRef

Zhang, L.M.[Lu-Ming], Gao, Y.[Yue], Zimmermann, R., Tian, Q.[Qi], Li, X.L.[Xue-Long],
Fusion of Multichannel Local and Global Structural Cues for Photo Aesthetics Evaluation,
IP(23), No. 3, March 2014, pp. 1419-1429.
IEEE DOI 1403
computer vision BibRef

Marchesotti, L.[Luca], Murray, N.[Naila], Perronnin, F.[Florent],
Discovering Beautiful Attributes for Aesthetic Image Analysis,
IJCV(113), No. 3, July 2015, pp. 246-266.
Springer DOI 1506
BibRef

Park, T., Zhang, B.,
Consensus Analysis and Modeling of Visual Aesthetic Perception,
AffCom(6), No. 3, July 2015, pp. 272-285.
IEEE DOI 1509
Analytical models BibRef

Zhang, Y.[Yanhao], Huang, Q.M.[Qing-Ming], Qin, L.[Lei], Zhao, S.[Sicheng], Lu, X.S.[Xiu-Sheng], Sun, X.S.[Xiao-Shuai], Yao, H.X.[Hong-Xun],
Strategy for aesthetic photography recommendation via collaborative composition model,
IET-CV(9), No. 5, 2015, pp. 691-698.
DOI Link 1511
BibRef
Earlier: A1, A6, A7, A3, A2, Only:
Aesthetic composition represetation for portrait photographing recommendation,
ICIP12(2753-2756).
IEEE DOI 1302
image reconstruction BibRef

Tian, X., Dong, Z., Yang, K., Mei, T.,
Query-Dependent Aesthetic Model With Deep Learning for Photo Quality Assessment,
MultMed(17), No. 11, November 2015, pp. 2035-2048.
IEEE DOI 1511
Adaptation models BibRef

Lu, X., Lin, Z., Jin, H., Yang, J., Wang, J.Z.,
Rating Image Aesthetics Using Deep Learning,
MultMed(17), No. 11, November 2015, pp. 2021-2034.
IEEE DOI 1511
Computer architecture BibRef

Lu, P.[Peng], Peng, X.[Xujun], Li, R.F.[Rui-Fan], Wang, X.J.[Xiao-Jie],
Towards aesthetics of image: A Bayesian framework for color harmony modeling,
SP:IC(39, Part C), No. 1, 2015, pp. 487-498.
Elsevier DOI 1601
Aesthetics assessment BibRef

Wang, W.N.[Wei-Ning], Zhao, W.J.[Wei-Jian], Cai, C.J.[Cheng-Jia], Huang, J.X.[Jie-Xiong], Xu, X.M.[Xiang-Min], Li, L.[Lei],
An efficient image aesthetic analysis system using Hadoop,
SP:IC(39, Part C), No. 1, 2015, pp. 499-508.
Elsevier DOI 1601
Image aesthetic analysis BibRef

Wang, W.N.[Wei-Ning], Zhao, M.Q.[Ming-Quan], Wang, L.[Li], Huang, J.X.[Jie-Xiong], Cai, C.J.[Cheng-Jia], Xu, X.M.[Xiang-Min],
A multi-scene deep learning model for image aesthetic evaluation,
SP:IC(47), No. 1, 2016, pp. 511-518.
Elsevier DOI 1610
Deep learning BibRef

Li, K.[Ke], Yan, B.[Bo], Li, J.[Jun], Majumder, A.[Aditi],
Seam carving based aesthetics enhancement for photos,
SP:IC(39, Part C), No. 1, 2015, pp. 509-516.
Elsevier DOI 1601
Seam carving BibRef

Cao, C.[Chong], Ai, H.Z.[Hai-Zhou],
Adaptive ranking of perceptual aesthetics,
SP:IC(39, Part C), No. 1, 2015, pp. 517-526.
Elsevier DOI 1601
Facial aesthetics BibRef

Ueno, T.[Taichi], Kajiyama, T.[Tomoko], Ouchi, N.[Noritomo],
A Method for Creating Package Images that Reflect Consumer Taste Impressions,
IEICE(E99-D), No. 1, January 2016, pp. 102-110.
WWW Link. 1601
BibRef

Hong, R.[Richang], Zhang, L.[Luming], Tao, D.C.[Da-Cheng],
Unified Photo Enhancement by Discovering Aesthetic Communities from Flickr,
IP(25), No. 3, March 2016, pp. 1124-1135.
IEEE DOI 1602
increasing the aesthetic appeal of a photo BibRef

Wu, X.X.[Xi-Xuan], Qiao, Y.[Yu], Wang, X.G.[Xiao-Gang], Tang, X.[Xiaoou],
Bridging Music and Image via Cross-Modal Ranking Analysis,
MultMed(18), No. 7, July 2016, pp. 1305-1318.
IEEE DOI 1608
image matching BibRef

Siahaan, E., Hanjalic, A., Redi, J.,
A Reliable Methodology to Collect Ground Truth Data of Image Aesthetic Appeal,
MultMed(18), No. 7, July 2016, pp. 1338-1350.
IEEE DOI 1608
Web sites BibRef

Sartori, A., Culibrk, D., Yan, Y., Job, R., Sebe, N.,
Computational Modeling of Affective Qualities of Abstract Paintings,
MultMedMag(23), No. 3, July 2016, pp. 44-54.
IEEE DOI 1609
art BibRef

Hernández-García, A., Fernández-Martínez, F., Díaz-de-María, F.,
Comparing visual descriptors and automatic rating strategies for video aesthetics prediction,
SP:IC(47), No. 1, 2016, pp. 280-288.
Elsevier DOI 1610
Automatic aesthetics prediction BibRef

Kao, Y., He, R., Huang, K.,
Deep Aesthetic Quality Assessment With Semantic Information,
IP(26), No. 3, March 2017, pp. 1482-1495.
IEEE DOI 1703
data visualisation 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

Jing, P.G.[Pei-Guang], Su, Y.T.[Yu-Ting], Nie, L.Q.[Li-Qiang], Gu, H.M.[Hui-Min],
Predicting Image Memorability Through Adaptive Transfer Learning From External Sources,
MultMed(19), No. 5, May 2017, pp. 1050-1062.
IEEE DOI 1704
Adaptation models BibRef

Sun, W.T., Chao, T.H., Kuo, Y.H., Hsu, W.H.,
Photo Filter Recommendation by Category-Aware Aesthetic Learning,
MultMed(19), No. 8, August 2017, pp. 1870-1880.
IEEE DOI 1708
Feature extraction, Image color analysis, Image quality, Neural networks, Object detection, Social network services, Aesthetic, convolutional neural network (CNN), filter recommendation, image quality, pairwise, comparison BibRef

Lee, H.J., Hong, K.S., Kang, H., Lee, S.,
Photo Aesthetics Analysis via DCNN Feature Encoding,
MultMed(19), No. 8, August 2017, pp. 1921-1932.
IEEE DOI 1708
Encoding, Feature extraction, Mathematical model, Neural networks, Quality assessment, Support vector machines, Training, Aesthetic attributes, deep convolutional neural network (DCNN), feature encoding, photo aesthetics, restricted, Boltzmann, machines BibRef

Deng, Y., Loy, C.C., Tang, X.,
Image Aesthetic Assessment: An experimental survey,
SPMag(34), No. 4, July 2017, pp. 80-106.
IEEE DOI 1708
Computational modeling, Computer vision, Feature extraction, Image processing, Machine learning, Neural networks, Support vector machines, Visualization BibRef

Nguyen, L.S.[Laurent Son], Ruiz-Correa, S.[Salvador], Mast, M.S.[Marianne Schmid], Gatica-Perez, D.[Daniel],
Check Out This Place: Inferring Ambiance From Airbnb Photos,
MultMed(20), No. 6, June 2018, pp. 1499-1511.
IEEE DOI 1805
Predict human response to the online photos. Multimedia communication, Observers, Psychology, Social network services, Urban areas, Airbnb, Ambiance prediction, social media BibRef

Lahrache, S.[Souad], El-Ouazzani, R.[Rajae], El-Qadi, A.[Abderrahim],
Rules of photography for image memorability analysis,
IET-IPR(12), No. 7, July 2018, pp. 1228-1236.
DOI Link 1806
BibRef

Kucer, M., Loui, A.C., Messinger, D.W.,
Leveraging Expert Feature Knowledge for Predicting Image Aesthetics,
IP(27), No. 10, October 2018, pp. 5100-5112.
IEEE DOI 1808
computer vision, convolution, feature extraction, feedforward neural nets, image classification, image retrieval, aesthetic quality assessment BibRef

Zhang, C.[Chao], Zhu, C.[Ce], Xu, X.[Xun], Liu, Y.P.[Yi-Peng], Xiao, J.M.[Ji-Min], Tillo, T.[Tammam],
Visual aesthetic understanding: Sample-specific aesthetic classification and deep activation map visualization,
SP:IC(67), 2018, pp. 12-21.
Elsevier DOI 1808
Visual aesthetic quality assessment, Aesthetic understanding, Sample-specific weighting BibRef

Guo, G., Wang, H., Shen, C., Yan, Y., Liao, H.M.,
Automatic Image Cropping for Visual Aesthetic Enhancement Using Deep Neural Networks and Cascaded Regression,
MultMed(20), No. 8, August 2018, pp. 2073-2085.
IEEE DOI 1808
convolution, feature extraction, feedforward neural nets, image classification, image enhancement, random-ferns regressor BibRef

Bereitschaft, B.[Bradley],
Mapping Creative Spaces in Omaha, NE: Resident Perceptions versus Creative Firm Locations,
IJGI(7), No. 7, 2018, pp. xx-yy.
DOI Link 1808
BibRef

Lemarchand, F.[François],
Fundamental visual features for aesthetic classification of photographs across datasets,
PRL(112), 2018, pp. 9-17.
Elsevier DOI 1809
Feature extraction, Aesthetic classification, Visual preferences, Deep learning, Neuroaesthetics BibRef

Lewandowska, A.[Anna], Samborska-Owczarek, A.[Anna], Dzisko, M.[Malwina],
Contrast Perception Across Human Cognitive Style,
ICIAR18(345-352).
Springer DOI 1807
Subjects' preferences to images with different contrast level is not only a function of image content but also of an individual pattern of perceptual organisation. BibRef

Aitamurto, T.[Tanja], Boin, J.B.[Jean-Baptiste], Chen, K.[Kaiping], Cherif, A.[Ahmed], Shridhar, S.[Skanda],
The Impact of Augmented Reality on Art Engagement: Liking, Impression of Learning, and Distraction,
VAMR18(II: 153-171).
Springer DOI 1807
BibRef

Kucer, M., Messinger, D.W.,
Aesthetic Inference for Smart Mobile Devices,
WACV18(1764-1773)
IEEE DOI 1806
cameras, feedforward neural nets, image classification, inference mechanisms, regression analysis, smart phones, Training BibRef

Schwarz, K., Wieschollek, P., Lensch, H.P.A.,
Will People Like Your Image? Learning the Aesthetic Space,
WACV18(2048-2057)
IEEE DOI 1806
data visualisation, feature extraction, image colour analysis, learning (artificial intelligence), mobile computing, Visualization BibRef

Furusho, Y., Kotani, K.,
Objective Subjective Evaluation Models of Pencil Still Drawings for Art Education,
DICTA17(1-5)
IEEE DOI 1804
art, art education, basic pencil, constructed evaluation model, evaluation word, factor Fi, features value, Shape BibRef

Mohseni, S.A., Wu, H.R., Thom, J.A.,
Automatic Recognition of Human Emotions Induced by Visual Contents of Digital Images Based on Color Histogram,
DICTA17(1-8)
IEEE DOI 1804
computer vision, emotion recognition, feature extraction, image colour analysis, optimisation, statistical analysis, ANOVA, Training BibRef

Amirshahi, S.A., Denzler, J.,
Judging Aesthetic Quality in Paintings Based on Artistic Inspired Color Features,
DICTA17(1-8)
IEEE DOI 1804
computer vision, feature extraction, image colour analysis, painting, support vector machines, 5-fold CV SVM, Wheels BibRef

Sudheendra, P., Jayagopi, D.B.,
Genre linked automated assessment and feedback of photographs based on visual aesthetics,
IPTA17(1-6)
IEEE DOI 1804
computer vision, data visualisation, visual perception, aesthetic quality, aspiring amateur photographer, visual signature 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

Liu, W.T.[Wen-Tao], Wang, Z.[Zhou],
A database for perceptual evaluation of image aesthetics,
ICIP17(1317-1321)
IEEE DOI 1803
Distributed databases, Histograms, Image color analysis, Image databases, Semantics, Training, image aesthetics assessment, subjective testing BibRef

Hii, Y.L., See, J., Kairanbay, M., Wong, L.K.,
Multigap: Multi-pooled inception network with text augmentation for aesthetic prediction of photographs,
ICIP17(1722-1726)
IEEE DOI 1803
Feature extraction, Image color analysis, Logic gates, Recurrent neural networks, Standards, Visualization, CNN, textual features BibRef

Kairanbay, M., See, J., Wong, L.K., Hii, Y.L.,
Filling the gaps: Reducing the complexity of networks for multi-attribute image aesthetic prediction,
ICIP17(3051-3055)
IEEE DOI 1803
Complexity theory, Computational modeling, Computer architecture, Feature extraction, Task analysis, Training, Visualization, Style BibRef

Fang, H.[Huidi], Cui, C.R.[Chao-Ran], Deng, X.[Xiang], Nie, X.S.[Xiu-Shan], Jian, M.E.[Muw-Ei], Yin, Y.L.[Yi-Long],
Image Aesthetic Distribution Prediction with Fully Convolutional Network,
MMMod18(I:267-278).
Springer DOI 1802
BibRef

Kairanbay, M.[Magzhan], See, J.[John], Wong, L.K.[Lai-Kuan],
Towards Demographic-Based Photographic Aesthetics Prediction for Portraitures,
MMMod18(I:531-543).
Springer DOI 1802
BibRef

Loh, Y.P., Tong, S., Liang, X., Kumada, T., Chan, C.S.,
Understanding Scenery Quality: A Visual Attention Measure and Its Computational Model,
WSM17(289-297)
IEEE DOI 1802
Computational modeling, Discrete wavelet transforms, Frequency-domain analysis, Proposals, Psychology, Visualization BibRef

Ren, J., Shen, X., Lin, Z., Mech, R., Foran, D.J.,
Personalized Image Aesthetics,
ICCV17(638-647)
IEEE DOI 1802
image processing, learning (artificial intelligence), Amazon Mechanical Turk, Flickr, active learning algorithm, Visualization BibRef

Wang, W., Shen, J.,
Deep Visual Attention Prediction,
IP(27), No. 5, May 2018, pp. 2368-2378.
IEEE DOI 1804
BibRef
Earlier:
Deep Cropping via Attention Box Prediction and Aesthetics Assessment,
ICCV17(2205-2213)
IEEE DOI 1802
learning (artificial intelligence), neural nets, object detection, CNN-based attention models, saliency detection. convolution, feature extraction, image classification, learning (artificial intelligence), BibRef

Chang, K.Y., Lu, K.H., Chen, C.S.,
Aesthetic Critiques Generation for Photos,
ICCV17(3534-3543)
IEEE DOI 1802
image processing, photography, AQ scoring, Photo Critique Captioning Dataset, Visualization BibRef

Huang, H.Z.[Hao-Zhi], Wang, H.[Hao], Luo, W.H.[Wen-Han], Ma, L.[Lin], Jiang, W.H.[Wen-Hao], Zhu, X.L.[Xiao-Long], Li, Z.F.[Zhi-Feng], Liu, W.[Wei],
Real-Time Neural Style Transfer for Videos,
CVPR17(7044-7052)
IEEE DOI 1711
Optical imaging, Optical losses, Optimization, Real-time systems, Training, Videos BibRef

Yang, S.[Shuai], Liu, J.Y.[Jia-Ying], Lian, Z.H.[Zhou-Hui], Guo, Z.M.[Zong-Ming],
Awesome Typography: Statistics-Based Text Effects Transfer,
CVPR17(2886-2895)
IEEE DOI 1711
Correlation, Distribution functions, Graphical models, Image color analysis, Shape, Skeleton, Standards BibRef

Chen, D.D.[Dong-Dong], Liao, J.[Jing], Yuan, L.[Lu], Yu, N.H.[Neng-Hai], Hua, G.[Gang],
Coherent Online Video Style Transfer,
ICCV17(1114-1123)
IEEE DOI 1802
BibRef
Earlier: A1, A3, A2, A4, A5:
StyleBank: An Explicit Representation for Neural Image Style Transfer,
CVPR17(2770-2779)
IEEE DOI 1711
image sequences, neural nets, optimisation, video signal processing, coherent online video style transfer, Video sequences. Convolution, Decoding, Kernel, Network architecture, Neural networks, Training BibRef

Ma, S., Liu, J., Chen, C.W.,
A-Lamp: Adaptive Layout-Aware Multi-patch Deep Convolutional Neural Network for Photo Aesthetic Assessment,
CVPR17(722-731)
IEEE DOI 1711
Feature extraction, Image resolution, Layout, Machine learning, Neural networks, Training BibRef

Bappy, J.H.[Jawadul H.], Barr, J.R.[Joseph R.], Srinivasan, N.[Narayanan], Roy-Chowdhury, A.K.[Amit K.],
Real Estate Image Classification,
WACV17(373-381)
IEEE DOI
PDF File. 1609
Which picture of the house is best. Computer vision, Feature extraction, Histograms, Image enhancement, Lighting, Neural networks, Standards Real Estate Image (REI) database. BibRef

Park, K., Hong, S., Baek, M., Han, B.,
Personalized Image Aesthetic Quality Assessment by Joint Regression and Ranking,
WACV17(1206-1214)
IEEE DOI 1609
Databases, Image quality, Quality assessment, Support vector machines, Testing, Training, Training, data BibRef

Gattupalli, V., Chandakkar, P.S., Li, B.X.[Bao-Xin],
A computational approach to relative aesthetics,
ICPR16(2446-2451)
IEEE DOI 1705
Convolutional codes, Data models, Image color analysis, Neural networks, Semantics, Training, Visualization BibRef

Kairanbay, M.[Magzhan], See, J.[John], Wong, L.K.[Lai-Kuan],
Aesthetic Evaluation of Facial Portraits Using Compositional Augmentation for Deep CNNs,
WFI16(II: 462-474).
Springer DOI 1704
BibRef

Lu, J.X.[Jia-Xin], Xu, M.[Mai], Wang, Z.L.[Zu-Lin],
Predicting the memorability of natural-scene images,
VCIP16(1-4)
IEEE DOI 1701
Animals BibRef

Hodgkinson, B.[Blake], Lutteroth, C.[Christof], Wünsche, B.[Burkhard],
glGetFeedback: Towards automatic feedback and assessment for OpenGL 3D modelling assignments,
ICVNZ16(1-6)
IEEE DOI 1701
Computational modeling. Analysis of graphics assignments. BibRef

Guo, L., Li, F., Liew, A.W.C.,
Image Aesthetic Evaluation Using Parallel Deep Convolution Neural Network,
DICTA16(1-5)
IEEE DOI 1701
Complexity theory BibRef

Thomas, C.[Christopher], Kovashka, A.[Adriana],
Seeing Behind the Camera: Identifying the Authorship of a Photograph,
CVPR16(3494-3502)
IEEE DOI 1612
180,000 images from 41 well-known photographers. BibRef

Mai, L., Jin, H., Liu, F.,
Composition-Preserving Deep Photo Aesthetics Assessment,
CVPR16(497-506)
IEEE DOI 1612
BibRef

Kong, S.[Shu], Shen, X.H.[Xiao-Hui], Lin, Z.[Zhe], Mech, R.[Radomir], Fowlkes, C.[Charless],
Photo Aesthetics Ranking Network with Attributes and Content Adaptation,
ECCV16(I: 662-679).
Springer DOI 1611
BibRef

Bianco, S.[Simone], Celona, L.[Luigi], Napoletano, P.[Paolo], Schettini, R.[Raimondo],
Predicting Image Aesthetics with Deep Learning,
ACIVS16(117-125).
Springer DOI 1611
BibRef

Lu, X.[Xin], Sawant, N.[Neela], Newman, M.G.[Michelle G.], Adams Jr., R.B.[Reginald B.], Wang, J.Z.[James Z.], Li, J.[Jia],
Identifying Emotions Aroused from Paintings,
Sketch16(I: 48-63).
Springer DOI 1611
BibRef

Tan, W.R., Chan, C.S., Aguirre, H.E., Tanaka, K.,
Ceci n'est pas une pipe: A deep convolutional network for fine-art paintings classification,
ICIP16(3703-3707)
IEEE DOI 1610
Convolution BibRef

Jin, B., Segovia, M.V.O., Süsstrunk, S.,
Image aesthetic predictors based on weighted CNNs,
ICIP16(2291-2295)
IEEE DOI 1610
Computational modeling BibRef

Hentschel, C., Wiradarma, T.P., Sack, H.,
Fine tuning CNNS with scarce training data: Adapting imagenet to art epoch classification,
ICIP16(3693-3697)
IEEE DOI 1610
Adaptation models BibRef

Puthenputhussery, A., Liu, Q., Liu, C.,
Color multi-fusion fisher vector feature for fine art painting categorization and influence analysis,
WACV16(1-9)
IEEE DOI 1606
Art BibRef

Lu, X., Lin, Z., Shen, X., Mech, R., Wang, J.Z.,
Deep Multi-patch Aggregation Network for Image Style, Aesthetics, and Quality Estimation,
ICCV15(990-998)
IEEE DOI 1602
Estimation BibRef

Pasupa, K.[Kitsuchart], Chatkamjuncharoen, P.[Panawee], Wuttilertdeshar, C.[Chotiros], Sugimoto, M.[Masanori],
Using Image Features and Eye Tracking Device to Predict Human Emotions Towards Abstract Images,
PSIVT15(419-430).
Springer DOI 1602
BibRef

Yu, J.Z.[Jin-Ze], Constable, M.[Martin], Wang, J.[Junyan], Chan, K.L.[Kap Luk], Brown, M.S.[Michael S.],
Aesthetic Interactive Hue Manipulation for Natural Scene Images,
PSIVT15(201-214).
Springer DOI 1602
BibRef

Lv, H.[Hao], Tian, X.M.[Xin-Mei],
Learning Relative Aesthetic Quality with a Pairwise Approach,
MMMod16(I: 493-504).
Springer DOI 1601
BibRef

She, B.[Bohao], Olson, C.F.[Clark F.],
WHAT2PRINT: Learning Image Evaluation,
ISVC15(II: 597-608).
Springer DOI 1601
BibRef

Paris, S.[Sylvain], Boyadzhiev, I.[Ivaylo], Bala, K.[Kavita], Adelson, E.H.[Edward H.],
Talk abstract: Computational lighting design and band-sifting operators,
ICIP15(36-37)
IEEE DOI 1512
Talk on project inspired by how photographers work. BibRef

Simond, F.[Florian], Arvanitopoulos, N.[Nikolaos], Susstrunk, S.[Sabine],
Image aesthetics depends on context,
ICIP15(3788-3792)
IEEE DOI 1512
aesthetic quality; classification; feature extraction BibRef

Wang, J.Y.[Jian-Yu], Allebach, J.[Jan],
Automatic assessment of online fashion shopping photo aesthetic quality,
ICIP15(2915-2919)
IEEE DOI 1512
Photo aesthetic quality BibRef

Kao, Y.Y.[Yue-Ying], Wang, C.[Chong], Huang, K.Q.[Kai-Qi],
Visual aesthetic quality assessment with a regression model,
ICIP15(1583-1587)
IEEE DOI 1512
Aesthetic image analysis; convolutional neural network; regression BibRef

Mavridaki, E.[Eftichia], Mezaris, V.[Vasileios],
A comprehensive aesthetic quality assessment method for natural images using basic rules of photography,
ICIP15(887-891)
IEEE DOI 1512
No-Reference image aesthetic quality assessment See also No-reference blur assessment in natural images using Fourier transform and spatial pyramids. BibRef

Simo-Serra, E.[Edgar], Fidler, S.[Sanja], Moreno-Noguer, F.[Francesc], Urtasun, R.[Raquel],
Neuroaesthetics in fashion: Modeling the perception of fashionability,
CVPR15(869-877)
IEEE DOI 1510
BibRef

Lu, P.[Peng], Kuang, Z.J.[Zhi-Jie], Peng, X.[Xujun], Li, R.F.[Rui-Fan],
Discovering Harmony: A Hierarchical Colour Harmony Model for Aesthetics Assessment,
ACCV14(III: 452-467).
Springer DOI 1504
BibRef

Sun, L.[Litian], Yamasaki, T.[Toshihiko], Aizawa, K.[Kiyoharu],
Relationship Between Visual Complexity and Aesthetics: Application to Beauty Prediction of Photos,
VISART14(20-34).
Springer DOI 1504
BibRef

Spratt, E.L.[Emily L.], Elgammal, A.[Ahmed],
Computational Beauty: Aesthetic Judgment at the Intersection of Art and Science,
VISART14(35-53).
Springer DOI 1504
BibRef

Crowley, E.J.[Elliot J.], Zisserman, A.[Andrew],
The Art of Detection,
CVAA16(I: 721-737).
Springer DOI 1611
BibRef
Earlier:
In Search of Art,
VISART14(54-70).
Springer DOI 1504
BibRef
And:
The State of the Art: Object Retrieval in Paintings using Discriminative Regions,
BMVC14(xx-yy).
HTML Version. 1410
BibRef
Earlier:
Of Gods and Goats: Weakly Supervised Learning of Figurative Art,
BMVC13(xx-yy).
DOI Link 1402
Greek vase decorations. See also Beazley Archive of Classical Art Pottery Database, The. BibRef

Alameda-Pineda, X., Ricci, E., Yan, Y., Sebe, N.,
Recognizing Emotions from Abstract Paintings Using Non-Linear Matrix Completion,
CVPR16(5240-5248)
IEEE DOI 1612
BibRef

Amirshahi, S.A.[Seyed Ali], Hayn-Leichsenring, G.U.[Gregor Uwe], Denzler, J.[Joachim], Redies, C.[Christoph],
JenAesthetics Subjective Dataset: Analyzing Paintings by Subjective Scores,
VISART14(3-19).
Springer DOI 1504
BibRef

Ma, S.[Shuang], Fan, Y.[Yangyu], Chen, C.W.[Chang Wen],
Finding your spot: A photography suggestion system for placing human in the scene,
ICIP14(556-560)
IEEE DOI 1502
Computational modeling Aesthetic composition rules. BibRef

Temel, D.[Dogancan], Al Regib, G.[Ghassan],
A comparative study of computational aesthetics,
ICIP14(590-594)
IEEE DOI 1502
Accuracy BibRef

Kim, J.[Jong_Hee], Kim, C.[Changick],
Aesthetic quality classification via subject region extraction,
ICIP14(536-540)
IEEE DOI 1502
Computer vision BibRef

Gaur, A.[Aarushi], Mikolajczyk, K.[Krystian],
Ranking Images Based on Aesthetic Qualities,
ICPR14(3410-3415)
IEEE DOI 1412
Clothing BibRef

Redi, M.[Miriam], O'Hare, N.[Neil], Schifanella, R.[Rossano], Trevisiol, M.[Michele], Jaimes, A.[Alejandro],
6 Seconds of Sound and Vision: Creativity in Micro-videos,
CVPR14(4272-4279)
IEEE DOI 1409
computational aesthetics; microvideo analysis; video creativity BibRef

Agrawal, A.[Abhishek], Premachandran, V.[Vittal], Kakarala, R.[Ramakrishna],
Rating Image Aesthetics Using a Crowd Sourcing Approach,
PSIVTWS13(24-32).
Springer DOI 1402
BibRef

Lo, K.Y.[Kuo-Yen], Liu, K.H.[Keng-Hao], Chen, C.S.[Chu-Song],
Intelligent Photographing Interface with On-Device Aesthetic Quality Assessment,
IWMV12(II:533-544).
Springer DOI 1304
BibRef
And:
Assessment of photo aesthetics with efficiency,
ICPR12(2186-2189).
WWW Link. 1302
BibRef

Redies, C.[Christoph], Amirshahi, S.A.[Seyed Ali], Koch, M.[Michael], Denzler, J.[Joachim],
PHOG-Derived Aesthetic Measures Applied to Color Photographs of Artworks, Natural Scenes and Objects,
VISART12(I: 522-531).
Springer DOI 1210
BibRef

Obrador, P.[Pere], Saad, M.A.[Michele A.], Suryanarayan, P.[Poonam], Oliver, N.M.[Nuria M.],
Towards Category-Based Aesthetic Models of Photographs,
MMMod12(63-76).
Springer DOI 1201
BibRef

Gdawiec, K.[Krzysztof], Kotarski, W.[Wieslaw], Lisowska, A.[Agnieszka],
Automatic Generation of Aesthetic Patterns with the Use of Dynamical Systems,
ISVC11(II: 691-700).
Springer DOI 1109
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:Nov 12, 2018 at 11:26:54