浏览全部资源
扫码关注微信
Received：28 March 2019，
Revised：2019-6-12，
Accepted：19 June 2019，
Published：16 December 2019
移动端阅览
目的
2
有关艺术作品审美感受的量化或计算，在心理学上已有许多研究。近年来，人工智能的研究成为热点，而对人类感知的定量分析也随之受到极大关注，例如基于图片或者音乐内容的情感计算等。美感作为一种艺术化的审美情感，与之相关的定量研究有较大潜力。为便于进行中国文化背景下的审美研究，同时为丰富图像情感与审美计算相关研究的数据基础，需建立一个国画美感和情感分析所用的图像数据库。
方法
2
从多种渠道收集筛选511幅国画素材及350个国画美感形容词，通过词汇筛选和因子分析获得国画美感主要因子；采用离散词汇和PAD（pleasure-arousal-dominance）情感连续维度空间这两种描述方式对国画的审美感受进行标注；对数据库进行情感和美感的模式分类，从而验证其实用性。
结果
2
获得5个国画美感主要语义标签：气势、清幽、生机、雅致和萧瑟；标注数据结果满足有效性验证；不同美感的PAD情感分布呈现一定极化；经测试，情感分类精度平均可达0.68，美感分类精度最高可达0.77。
结论
2
本文得到的5个国画美感评价范畴，可基本概括国画的审美感受；所建立的数据库，能为视觉美感及情感的定量研究或者计算机视觉、实验美学等领域的研究提供有效数据基础；PAD分布对美感有较好区分性。下一步将进一步扩充数据库，以解决数据分布不均问题，同时进一步挖掘PAD分布与美感分布之间的关联。
Objective
2
Artificial intelligence has been a popular issue in recent years. Therefore
quantitative analysis of human perception
such as affective computing based on picture or music materials
has elicited much concern. One of the most important events in image aesthetic research is the introduction of experimental psychology methods to establish the relationship between the subjective affective state and objective artworks. Recent developments inempirical aesthetics in the general cultural background have heightened the need for parallel research on single cultural background. Traditional Chinese art is part and parcel of the world culture. Central to conducting quantification research on the art perception and affective computing of Chinese paintings and enriching the database of general aesthetics and emotion is building an image database for aesthetic and emotion analyses of traditional Chinese paintings. To this end
we introduce a new image aesthetic database for aesthetic and emotion analyses of Chinese paintings. The database contains over 500 images of Chinese paintings in five semantic aesthetic categories and quantitative annotations of the three-dimensional emotion score and aesthetic quality of each image.
Method
2
To accumulate basic data
511 traditional Chinese paintings are collected and filtered as digital images from multiple sources(e.g.
www.artsjk.com)
and 350 adjectives are gathered through extensive provenance(e.g.
Historical Dictionary of Aesthetics and classical documents in psycho-aesthetics
art aesthetics
and philosophical aesthetics). Two methods are used in annotating Chinese paintings:the discrete emotion model and the pleasure-arousal-dominance (PAD) scale. Discrete emotion theory claims that a small number of core emotions exists. In the PAD emotional state model
the pleasure-displeasure scale measures how pleasant or unpleasant one feels about something
the arousal-non-arousal scale measures how energized or soporific one feels
and the dominance-submissiveness scale represents how controlling and dominant versus controlled or submissive one feels. The major differences between the two models pertain to the low resolution of the discrete model in discriminating affectively vague examples and the difficulty in understanding part subjects in the PAD scale. Therefore
the combination of the two approaches is necessary. First
to build the basic concepts of our subjective annotation
a questionnaire survey is conducted to select favorable adjectives for describing affective feelings when appreciating Chinese paintings. Participants are asked to answer if they think the adjective is applicable for representing aesthetic feelings when appreciating a Chinese painting (yes or no)
and from the adjectives selected
more than 50% are chosen as meaningful. Second
subjective assessment and factor analysis are adopted to conduct a pilot study of the principle factors of aesthetics in Chinese paintings based on the adjectives collected previously. Responses are received from 40 participants who rated each item with regard to how frequently they use it to describe their emotional reaction in Chinese painting appreciation (1 never; 5 very frequently). Two groups of participants
namely
experts (50%) and amateurs (50%)
are investigated. With the factor analysis method
5 aesthetic semantic categories and 25 secondary aesthetic concepts of the principle factors are obtained for annotation in the discrete adjective method. Third
the aesthetic style and affective response of the collected paintings are annotated. The participants are asked to make a judgement of the aesthetic category and rate the aesthetic quality and PAD value of a painting. Fourth
statistical analysis is performed to calculate the distributions of aesthetics and emotions in the annotating experiment. Two parameters
namely
aesthetic membership vector and aesthetic average intensity
are designed to measure the ratings and frequencies of different aesthetics and calculate the distribution of aesthetic judgements. In addition
the distribution of mean values and the standard deviations of PAD scores are computed. Then
an analysis between aesthetic feelings and emotional responses is performed to determine the effects of emotion distribution on aesthetic classification. Finally
to identify the utility of the database
emotion and aesthetic pattern classification is conducted using various methods. Regression analysis using various models is performed between the image feature and PAD value
and pattern classification of five aesthetic categories based on different classifiers is conducted.
Result
2
The following five aesthetic categories of traditional Chinese paintings are identified:Qishi(mighty
magnificent
glorious
grandeur
vigorous and firm
precipitous
powerful in strength and impetus
towering
tremendous
boundless
bold and unconstrained
and extremely attractive and impressive)
Qingyou(quiet and beautiful
ethereal
distant
solemn
flexible and elusive
tranquil
and extremely delicate and light)
Shengji(full of life
vivid
full of vitality
smart
spirited
and characterized by spirit and animation)
Yazhi(elegant
refined
pure and classic
layered
and designed well)
and Xiaose(bleak
empty and without people
and makes people feel sad or frightened).The test-retest reliability and Cronbach's alpha of the PAD ratings verify the credibility of the database. The distribution of aesthetic categories and PAD emotional ratings shows a selection bias in the perception of Chinese paintings with positive and dynamic feelings. The mean classification accuracy of emotion is 0.68
and the highest classification of aesthetics is 0.77.
Conclusion
2
This study identifies five semantic categories of aesthetics of Chinese paintings. Experiments confirm that these categories can cover most paintings in Chinese painting appreciation. A database is established based on the five categories
and the emotional responses and aesthetic style and quality of the collected paintings are confirmed in the subjective assessment. The database shows great diversity in artistic style and emotional expression. By pattern classification of emotion polarity and aesthetic label
the effectiveness of the extra-trees classifier through uneven data is tested and proven. The accuracy of emotion and aesthetic classification illustrates that the regression and classification methods presented in this paper are effective. We believe that this database can be used forthe quantitative study of visual beauty
computer vision
affective computing
and experimental aesthetics. Our future work will include expanding the data of rare aesthetics (e.g.
Qingyou and Xiaose) and conducting multi-label aesthetic classification based on the PAD affective model of images in the database.
Bao H. 2012. Research on visual-perception based automatic classification techniques for Chinese painting images. Beijing:Beijing Jiaotong University, 25-26
鲍泓. 2012.基于视觉感知的中国画图像语义自动分类研究.北京:北京交通大学, 25-26
Belyayev, Novikov and Tolstach. 1993. Dictionary of Aesthetics. Translate by Tang X S. Beijing: Oriental Press: 9-20
别利亚耶夫, 诺维科夫, 托尔斯特赫. 1993.美学辞典.汤侠生, 译.北京: 东方出版社: 9-20
Bishop C M. 2006. Pattern recognition and machine learning. Berlin Heidelberg: Springer
Cattell R B. 1978. The Scientific Use of Factor Analysis in Behavioral and Life Sciences. Boston, MA: Springer
Chen L J. 2010. The relationship between aesthetic experience and positive emotion and the impact on change detection. Chongqing:Southeast University, 39-43
陈丽君. 2010.美感与积极情绪的关系及对变化觉察的影响.重庆:西南大学, 39-43
Chih C C and Chih J L. 2011. LIBSVM:a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2(27):1-27[DOI:10.1145/1961189.1961199]
Cohen P, Stephen G W and Leona S A. 2003. Applied multiple regression/correlation analysis for the behavioral sciences. Third edition. Mahwah, New Jersey: Lawrence Erlbaum Associates&Publishers, 193-194
Cronbach L J. 1951. Coefficient alpha and the internal structure of tests. Psychometrika, 16(3):297-334[DOI:10.1007/BF02310555]
Dong X W. 2003. An Introduction to Aesthetics. Beijing:Peking University Press:38-65
董学文. 2003.美学概论.北京:北京大学出版社:38-65
Ding Y H. 2008. A research on the role of aesthetic experience ofconcept metaphor understanding to the scientific concept understanding. Chongqing:Southwest University, 87-90
丁月华. 2008.概念隐喻理解中的美感体验对科学概念理解的作用研究.重庆:西南大学, 87-90]
Feng Q. 1985. Encyclopedia of philocophy. Shanghai:Shanghai Word Press:1-35
冯契. 1985.哲学大辞典.上海:上海辞书出版社:1-35
Friedman J H. 2001. Greedy function approximation:A gradient boosting machine. Annals of Statistics, 29(5):1189-1232[DOI:10.1214/aos/1013203451]
Gao F, Nie J, Huang L, Duan L Y and Li X M. 2017. Traditional Chinese painting classification based on painting techniques. Chinese Journal of Computers, 40(12):2871-2882
高峰, 聂婕, 黄磊, 段凌宇, 李晓明. 2017.基于表现手法的国画分类方法研究.计算机学报, 40(12):2871-2882[DOI:10.11897/SP.J.1016.2017.02871]
Geurts P, Ernst D and Wehenkel L. 2006. Extremely randomized trees. Machine Learning, 63(1):3-42[DOI:10.1007/s10994-006-6226-1]
Gu J H and Zhang Z G. 1999. Aesthetics and Aesthetics Educational Dictionary. Beijing:Academy Press:69-71
顾建华, 张占国. 1999.美学与美育词典.北京:学苑出版社:69-71
Hager M, Hagemann D, Danner D and Schankin A. 2012. Assessing aesthetic appreciation of visual artworks-the construction of the Art Reception Survey (ARS). Psychology of Aesthetics, Creativity, and the Arts, 6(4):320-333[DOI:10.1037/a0028776]
Hagtvedt H, Patrick V M and Hagtvedt R. 2008. The perception and evaluation of visual art. Empirical Studies of the Arts, 26(2):197-218[DOI:10.2190/EM.26.2.d]
Hevner K. 1936. Experimental studies of the elements of expression in music. The American Journal of Psychology, 48(2):246-268[DOI:10.2307/1415746]
Huber P J. 1981. Robust Statistics. New York:John Wiley and Sons
Israeli N. 1928. Affective reactions to painting reproductions:a study in the psychology of esthetics. Journal of Applied Psychology, 12(1):125-139[DOI:10.1037/h0070445]
Joshi D, Datta R, Fedorovskaya E, Luong Q T, Wang J Z, Li J and Luo J B. 2011. Aesthetics and emotions in images. IEEE Signal Processing Magazine, 28(5):94-115[DOI:10.1109/MSP.2011.941851]
Ke Y, Tang X O and Jing F. 2016. The design of high-level features for photo quality assessment//Proceedings of 2016 IEEE Computer Vision and Pattern Recognition.New York:IEEE, 419-426[DOI:10.1109/CVPR.2006.303]
Koch C and Ullman S. 1987. Shifts in selective visual attention:towards the underlying neural circuitry//VainaL M. Matters of Intelligence:Conceptual Structures in Cognitive Neuroscience.Human neurobiology, Dordrecht:Springer, 4(2):115-141[DOI:10.1007/978-94-009-3833-5_5]
Korn F, Sidiropoulos N, Faloutsos C, Siegel E and Protopapas Z. 2008. Fast Nearest Neighbor Search in Medical Image Databases[EB/OL]. 2008-12-02[2019-03-13] . http://www-db.disi.unibo.it/courses/SI-LS/papers/KFS+96.pdf http://www-db.disi.unibo.it/courses/SI-LS/papers/KFS+96.pdf
Konstantinov, Angenov. 1987. Music Aesthetic principles. Beijing:China Federation of Literary&Art Circles Publishing Corp:23-40
康斯坦丁诺夫, 安盖诺夫. 1987.音乐美学原理.北京:中国文联出版公司:23-40
Lang P J, Bradley M M and Cuthbert B N. 1997. International affective picture system (IAPS): Technical Manual and Affective Ratings. Gainesville, FL: University of Florida
Li X, Lu G M, Yan J J and Zhang Z Y. 2018. A survey of dimensional emotion prediction by multimodal cues. Acta Automatica Sinica, 44(12):2142-2159
李霞, 卢官明, 闫静杰, 张正言. 2018.多模态维度情感预测综述.自动化学报, 44(12):2142-2159[DOI:10.16383/j.aas.2018.c170644]
Li Y Z, Sheng J C and Hua B. 2018. Improved embedded learning for classification of Chinese paintings. Journal of Computer-Aided Design&Computer Graphics, 30(5):893-900
李玉芝, 盛家川, 华斌. 2018.中国画分类的改进嵌入式学习算法.计算机辅助设计与图形学学报, 30(5):893-900[DOI:10.3724/SP.J.1089.2018.16539]
Liu Y, Tao L M and Fu X L. 2009. The analysis of PAD emotional state model based on emotion pictures. Journal of Image and Graphics, 14(5):753-758
刘烨, 陶霖密, 傅小兰. 2009.基于情绪图片的PAD情感状态模型分析.中国图象图形学报, 14(5):753-758[DOI:10.11834/jig.20090501]
Luo W, Wang X G and Tang X O. 2012. Content-based photo quality assessment//Proceedings of 2011 IEEE International Conference on Computer Vision. Barcelona, Spain:IEEE, 2206-2213[DOI:10.1109/ICCV.2011.6126498]
Mackay D J C. 1994. Bayesian nonlinear modeling for the prediction competition. ASHRAE Transactions, 100(2):221-234[DOI:10.1007/978-94-015-8729-7_18]
Marković and Slobodan. 2010. Aesthetic experience and the emotional content of paintings. Psihologija, 43(1):47-64[DOI:10.2298/PSI1001047M]
Mehrabian A. 1996. Pleasure-arousal-dominance:a general framework for describing and measuring individual differences in Temperament. Current Psychology, 14(4):261-292[DOI:10.1007/BF02686918]
Meng Z H. 2008. Experimental Psychological Method of Subjective Evaluation of Sound Quality. Beijing:National Defense Industry Press
孟子厚. 2008.音质主观评价的实验心理学方法.北京:国防工业出版社
Mohammad S M and Kiritchenko S. 2018. WikiArtemotions: an annotated dataset of emotions evoked by art[EB/OL]. 2018-02-26[2019-03-13] . https://svkir.com/papers/Mohammad-Kiritchenko-WikiArt-LREC-2018.pdf https://svkir.com/papers/Mohammad-Kiritchenko-WikiArt-LREC-2018.pdf
Murphy and Kevin P. 2012. Machine learning: a Probabilistic Perspective. MIT press. Massachusetts: The MIT Press
Murray N, Marchesotti L andPerronnin F. 2012. AVA:a large-scale database for aesthetic visual analysis//Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition. Providence, RI, USA:IEEE, 2408-2415[DOI:10.1109/CVPR.2012.6247954]
Quinlan J R. 1986. Induction of Decision Trees. Machine Learning, 1(1):81-106[DOI:10.1023/A:1022643204877]
Rowold J. 2008. Instrument development for esthetic perception assessment. Journal of Media Psychology:Theories, Methods, and Applications, 20(1):35-40[DOI:10.1027/1864-1105.20.1.35]
Shao D Z. 1999. The similarities and differences between Chinese landscape painting and Western landscape painting:on the history, present situation and prospect of the blending of the two. Literature and Art Studies, (4):57-69
邵大箴. 1999.中国山水画与西方风景画的同和异——兼论两者交融的历史、现状与前景.文艺研究, (4):57-69
Shao L Y, Ma C Y and Wang B M. 2002. Chinese Fine Arts Dictionary. Shanghai:Shanghai Word Press:12-16
邵洛羊, 马承源, 王伯敏. 2002.中国美术大辞典.上海:上海辞书出版社:12-16
Silvia P J, Fayn K, Nusbaum E C, Emily C and Beaty R E. 2015. Openness to experience and awe in response to nature and music:personality and profound aesthetic experiences. Psychology of Aesthetics, Creativity, and the Arts, 9(4)376-384[DOI:10.1037/aca0000028]
Stamatopoulou D. 2004. Integrating the philosophy and psychology of aesthetic experience:development of the aesthetic experience scale. Psychological Reports, 95(2):673-695[DOI:10.2466/pr0.95.2.673-695]
Sun X, Ye J Q, Long R T and Ren F J. 2014. Sentiment analysis of Chinese microblog based on emotional semantic words dictionary and PAD model. Journal of Shanxi University (Natural Science Edition), 37(4):580-587
孙晓, 叶嘉麒, 龙润田, 任福继. 2014.基于情感语义词典与PAD模型的中文微博情感分析.山西大学学报:自然科学版, 37(4):580-587[DOI:10.13451/j.cnki.shanxi.univ(nat.sci.).2014.04.017]
Toshio Takeuchi. 1987. Aesthetic Encyclopedia. Translate by Chi X Z. Heilongjiang: Heilongjiang People's Press: 2-8
竹内敏雄. 1987.美学百科词典.池学镇, 译.黑龙江: 黑龙江人民出版社: 2-8
You S T. 2018. A gestalt analysis of GengLuo's selected works. Northern Music, 38(8):77-78, 88
游师庭. 2018.罗赓音乐的艺术价值初探——以格式塔分析原则为例.北方音乐, 38(8):77-78, 88
Ye L. 1999. Modern Aesthetic System. Beijing:Peking University Publishers:1-35
叶朗. 1999.现代美学体系.北京:北京大学出版社:1-35
Zhong J and Qian M Y. 2005. A study of development and validation of Chinese mood adjective check list 13(1): 9-13
钟杰, 钱铭怡.中文情绪形容词检测表的编制与信效度研究.中国临床心理学杂志, 2005, 13(1): 9-13[ DOI:10.3969/j.issn.1005-3611.2005.01.003 http://dx.doi.org/10.3969/j.issn.1005-3611.2005.01.003 ]
Zhou Z H. 2016. Machine Learning. Beijing:Tsinghua University Press
周志华. 2016.机器学习.北京:清华大学出版社
相关作者
相关机构
京公网安备11010802024621
