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.Z.[Yu-Zhen],
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.H.[Yan-Hao],
Huang, Q.M.[Qing-Ming],
Qin, L.[Lei],
Zhao, S.H.[Sic-Heng],
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.J.[Xu-Jun],
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.C.[Ri-Chang],
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
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, 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
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
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.[H. Ren],
A Color Moments-Based System for Recognition of Emotions Induced by
Color Images,
IVCNZ19(1-6)
IEEE DOI
2004
emotion recognition, feature extraction, image classification,
image colour analysis, emotion recognition, image indexing,
recognition of emotions.
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
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
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
data visualisation, visual perception,
aesthetic quality, aspiring amateur photographer,
visual signature
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,
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
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
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.
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
Hodgkinson, B.[Blake],
Lutteroth, C.[Christof],
Wünsche, B.C.[Burkhard C.],
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
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.C.[Charless C.],
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.Y.[Jun-Yan],
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.J.[Xu-Jun],
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.Y.[Yang-Yu],
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
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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
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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
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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.],
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Towards Category-Based Aesthetic Models of Photographs,
MMMod12(63-76).
Springer DOI
1201
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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
Point Cloud Generation, Point Cloud Synthesis .