浏览全部资源
扫码关注微信
Received：12 December 2017，
Revised：2018-2-8，
Published：16 July 2018
移动端阅览
目的
2
图像分类与情感分析是当前计算机视觉领域的研究热点，为人类绘画图像数字化研究提供了有效方法，在人类绘画艺术作品保护与作品创新中具有重要的应用价值。为更好地实现绘画作品的研究与创新，本文主要针对现阶段国内外的绘画分类与情感分析相关文献，进行详细地整理与分析。
方法
2
本文以广泛的文献研究为基础，分析中西方绘画的不同表征方式及形成原因，归纳总结支持向量机、决策树、人工神经网络和深度学习等绘画图像分类中常用机器学习方法，并概述各类方法的优劣；重点围绕绘画图像特征提取与分类，绘画情感分析两个方面，对当前文献进行了系统地分析和总结。
结果
2
系统概括了当前绘画图像研究中常用绘画数据库；以绘画图像的笔触特征、颜色特征、形状特征和纹理特征、留白特征等方面为依据，详细综述了中西方绘画特征提取技术与分类方法的研究现状及发展；简要梳理了绘画图像分类模型中常用的评价方法，并分析了当前研究中的常用评价指标；主要从颜色特征的角度出发，阐述了西方绘画情感分析的研究进展，为中国传统绘画情感分析提供了有效的思路；最后，提出了当前绘画分类和绘画情感研究中存在的问题和挑战，并探讨了存在问题的应对之策。
结论
2
绘画作为人类重要的文化成果，未来会涌现出更多的研究算法与探索思路，本文内容对绘画图像分类的进一步研究，特别是中国传统水墨画情感分析和绘画艺术创作方面的研究，可以起到一定的启发和指导作用。
Objective
2
Image classification and affective analysis are popular issues in the field of computer vision
provide effective methods for digital painting
and play important roles in art protection and painting innovation. Brush stroke
colors
shapes
textures
and white spaces are important visual features of paintings. Classification based on these visual features can help identification of painting style and painter
analyze painting affection
further understand the meanings of painting creation and inherited cultures. In order to better realize the research and innovation of painting
this paper provides a comprehensive survey and analysis
focusing on domestic and international research on painting classification and affective analysis at present.
Method
2
Based on the extensive literature research
the paper firstly shows the different representation modes of Chinese and western paintings and then analyzes the reason for the differences between these two modes. The reasons are mainly derived from the various cultural backgrounds and the different ways of thinking. Chinese traditional painting has a unique artistic expression technique
which accounts for the artistic "false or true complement" effect produced by the skillful use of white space. In addition
traditional Chinese painting attaches importance to the combination of calligraphy and poetry
is decorated with seals
and emphasizes the connection between art and nature to present spirit by form. Line is the basic modeling approach and color is the auxiliary characteristic of Chinese traditional painting. The bright and dark changes of light and shadow are not emphasized. Besides
line is a form of expression of the affective characteristics of traditional Chinese painting. For an appreciator
Chinese traditional painting requires more association and imagination than mere visual effect. The Chinese painting style mainly includes two major categories
namely
traditional ink paintings and murals. The formation of the ink-and-wash style is mainly distinguished by representative painters
such as Qi Baishi
Zheng Banqiao
Xu Beihong
Wu Guanzhong
Wu Changshuo
and Huang Gongwang. Mural research is represented by mogao grottoes in Dunhuang which have a distinctive national style characterized by rich and colorful content and a form of painting that embodies the expectations of people's good wishes. Western painting is distinct from traditional Chinese painting. Traditional western painting highlights realism
similarities in appearance
reproduction
space-time
and effect of light colors. Western painting attaches great importance to the change in color
light
and shadow to portray images. In addition
the elaborate and tactful use of color can reflect painting affection. An entire painting exhibits a good sense of texture and space because of the object shading. Contrary to traditional Chinese painting
western painting provides viewers with more visual effects. The Western painting style is associated with the development of the literature and art movement
and the formation of painting style is mainly divided into baroque
three-dimensional impressionist
romantic
Rococo
and Renaissance styles. Secondly
the paper outlines the machine learning methods of support vector machine
decision tree
artificial neural network
deep learning
which are commonly used in painting classification
and analyze the advantages and disadvantages of these methods. Moreover
this paper systematically analyzes and summarizes the current literatures
focusing on the two aspects of feature extraction and classification of painting
affective analysis of painting.
Result
2
On the basis of current literatures related
this paper sums up the painting database commonly used in the present study. Moreover
it reviews in detail the research status and the development of feature extraction technology and classification methods for Chinese and western paintings in terms of such characteristics as brush strokes
color features
shape features
texture features and white features. It also briefly describes the commonly used evaluation methods of painting classification model
which are error rate and accuracy
precision and recall ratio
P-R curve and F1 measurement
and ROC curve and AUC. And several commonly used evaluation indexes in current studies are analyzed. Computer vision technology has a distinct advantage in the fields of object recognition
scene classification
image classification
and affective and semantic image analysis
which can meaningfully evaluate the perception of target images and scenes by simulating human visual ability. The essential feature of the image is the key to accurate judgement. As an image resource
the selection of painting characteristics is important in painting classification and affective analysis. Closely related to painting feature selection and classification research
machine learning is a way that investigates how to use a computer to simulate or realize people's learning activities to acquire new knowledge and skills. Machine learning is widely used in artificial intelligence. Furthermore
the paper probes the affective investigation of Western painting on the basis of color features and provides an efficient idea for the affective analysis of Chinese traditional painting. Paintings can reflect the objective social life and rich affection of their painters
and using computer intelligence to analyze the painting affection can help us understand the history and culture of various periods well. Chinese painting has a unique style in terms of ink brush stroke
ink shade
white space
and painting content
which can also convey the different affections that the painters aim to express. Combining cognitive and psychological knowledge
image feature extraction methods can also realize the affective analysis and extraction of Chinese ink-and-wash paintings. Finally
using a database of painting classification
classification goals
and the limitations of affective analysis of paintings
this study highlights the existing problems and challenges in the study of painting classification and affective analysis and discusses solutions to these existing problems.
Conclusion
2
As an important cultural achievement of human
more and more painting research algorithms and exploring thinks will emerge in the further. This article can provide guidance to further studies on painting classification
especially in the research of traditional Chinese ink painting affective analysis and painting art creation.
Fang L Y. Digital imaging technology used in the research and thinking of contemporary printmaking[D]. Chongqing: Sichuan Fine Arts Institute, 2013.
房丽媛. 数码图像技术应用于当代版画创作的研究及思考[D]. 重庆: 四川美术学院, 2013.
Wang J. Research on image classification algorithm based on color feature[D]. Dalian: Dalian Maritime University, 2008.
王静. 基于颜色特征的图像分类算法研究[D]. 大连: 大连海事大学, 2008.
Lyu P X. Research on image emotion categorization[D]. Qinhuangdao: Yanshan University, 2014.
吕鹏霄. 图像情感分类研究[D]. 秦皇岛: 燕山大学, 2014.
Yang B. Research on painting image classification based on aesthetic style[D]. Hangzhou: Zhejiang University, 2013.
杨冰. 基于艺术风格的绘画图像分类研究[D]. 杭州: 浙江大学, 2013.
Sheng J C. Computerized learning and classification of traditional Chinese IWPs (Ink and Wash Paintings)[D]. Tianjin: Tianjin University, 2013.
盛家川. 中国传统水墨画的计算机分类与算法研究[D]. 天津: 天津大学, 2013.
Shamir L, Macura T, Orlov N, et al. Impressionism, expressionism, surrealism:automated recognition of painters and schools of art[J]. ACM Transactions on Applied Perception, 2010, 7(2):#8.[DOI:10.1145/1670671.1670672]
Widjaja I, Leow W K, Wu F C. Identifying painters from color profiles of skin patches in painting images[C]//Proceedings of 2003 International Conference on Image Processing. Barcelona, Spain, 2003: I-845-8.[ DOI:10.1109/ICIP.2003.1247095 http://dx.doi.org/10.1109/ICIP.2003.1247095 ]
Khan F S, Van De Weijer J, Vanrell M. Who painted this painting?[C]//Proceedings of the CREATE 2010 Conference, Massachusetts. USA, 2010: 329-333.
Gunsel B, Sariel S, Icoglu O. Content-based access to art paintings[C]//Proceedings of 2005 International Conference on Image Processing. Genova, Italy: IEEE, 2005: Ⅱ-558-61.[ DOI:10.1109/ICIP.2005.1530116 http://dx.doi.org/10.1109/ICIP.2005.1530116 ]
Zujovic J, Gandy L, Friedman S, et al. Classifying paintings by artistic genre: an analysis of features & classifiers[C]//Proceedings of 2009 IEEE International Workshop on Multimedia Signal Processing. Rio De Janeiro, Brazil: IEEE, 2009: 1-5.[ DOI: 10.1109/MMSP.2009.5293271 http://dx.doi.org/10.1109/MMSP.2009.5293271 ]
Arora R S, Elgammal A. Towards automated classification of fine-art painting style: a comparative study[C]//Proceedings of the 21st International Conference on Pattern Recognition. Tsukuba, Japan: IEEE, 2012: 3541-3544.
Li J, Wang J Z. Studying digital imagery of ancient paintings by mixtures of stochastic models[J]. IEEE Transactions on Image Processing, 2004, 13(3):340-353.[DOI:10.1109/TIP.2003.821349]
Kang D, Shim H, Yoon K. A method for extracting emotion using colors comprise the painting image[J]. Multimedia Tools and Applications, 2018, 77(4):4985-5002.[DOI:10.1007/s11042-017-4667-0]
Shi R L. The thinking of differences between Chinese and Western painting[J]. Journal of Jilin Radio and TV University, 2007, (5):88-90, 104.
史荣利.中西方绘画的差异思考[J].吉林广播电视大学学报, 2007, (5):88-90, 104.[DOI:10.3969/j.issn.1008-7508.2007.05.029]
Fan Z B, Li Y N, Yu J H, et al. Visual complexity of Chinese ink paintings[C]//Proceedings of ACM Symposium on Applied Perception. Cottbus, Germany: ACM, 2017: #9.[ DOI: 10.1145/3119881.3119883 http://dx.doi.org/10.1145/3119881.3119883 ]
Florea C, Condorovici R, Vertan C, et al. Pandora: description of a painting database for art movement recognition with baselines and perspectives[C]//Proceedings of the 24th European Signal Processing Conference. Budapest, Hungary: IEEE, 2016: 918-922.[ DOI：10.1109/EUSIPCO.2016.7760382 http://dx.doi.org/10.1109/EUSIPCO.2016.7760382 ]
Florea C, Toca C, Gieseke F. Artistic movement recognition by boosted fusion of color structure and topographic description[C]//Proceedings of 17th IEEE Winter Conference on Applications of Computer Vision. Santa Rosa, CA, USA: IEEE, 2017: 569-577.[ DOI: 10.1109/WACV.2017.69 http://dx.doi.org/10.1109/WACV.2017.69 ]
Condorovici R G, Florea C, Vrnceanu R, et al. Perceptually-inspired artistic genre identification system in digitized painting collections[C]//Proceedings of the 18th Scandinavian Conference on Image Analysis. Espoo, Finland: Springer, 2013: 687-696.[ DOI: 10.1007/978-3-642-38886-6_64 http://dx.doi.org/10.1007/978-3-642-38886-6_64 ]
Van Noord N, Postma E. Learning scale-variant and scale-invariant features for deep image classification[J]. Pattern Recognition, 2017, 61:583-592.[DOI:10.1016/j.patcog.2016.06.005]
Sheng J C, Jiang J M. Recognition of Chinese artists via windowed and entropy balanced fusion in classification of their authored ink and wash paintings (IWPs)[J]. Pattern Recognition, 2014, 47(2):612-622.[DOI:10.1016/j.patcog.2013.08.017]
Sun M J, Zhang D, Wang Z, et al. Monte Carlo Convex Hull model for classification of traditional Chinese paintings[J]. Neurocomputing, 2016, 171:788-797.[DOI:10.1016/j.neucom.2015.08.013]
Yang B, Xu D Q. Color boosted visual saliency detection and its application to image classification[J]. Multimedia Tools and Application, 2014, 69(3):877-896.[DOI:10.1007/s11042-012-1148-3]
Yang B, Xu D Q, Tang D W, et al. Aesthetic visual style assessment on Dunhuang murals[J]. Journal of Shanghai Jiaotong University (Science), 2014, 19(1):28-34.[DOI:10.1007/s12204-014-1473-y]
Johnson C R, Hendriks E, Berezhnoy I J, et al. Image processing for artist identification[J]. IEEE Signal Processing Magazine, 2008, 25(4):37-48.[DOI:10.1109/MSP.2008.923513]
Shahram M, Stork D G, Donoho D. Recovering layers of brush strokes through statistical analysis of color and shape: an application to van Gogh's Self portrait with grey felt hat [C]//Proceeding of the SPIE 6180, Computer Image Analysis in the Study of Art. San Jose, California, United States: SPIE, 2008: #68100D.[ DOI：10.1117/12.765773 http://dx.doi.org/10.1117/12.765773 ].
Li J, Yao L, Hendriks E, et al. Rhythmic brushstrokes distinguish van Gogh from his contemporaries:findings via automated brushstroke extraction[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(6):1159-1176.[DOI:10.1109/TPAMI.2011.203]
Meer P, Georgescu B. Edge detection with embedded confidence[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(12):1351-1365.[DOI:10.1109/34.977560]
Jiang S Q, Huang Q M, Ye Q X, et al. An effective method to detect and categorize digitized traditional Chinese paintings[J]. Pattern Recognition Letters, 2006, 27(7):734-746.[DOI:10.1016/j.patrec.2005.10.017]
Cristianini N, Shawe-Taylor J. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods[M]. Cambridge:Cambridge University Press, 2000.
Niu X X, Suen C Y. A novel hybrid CNN-SVM classifier for recognizing handwritten digits[J]. Pattern Recognition, 2012, 45(4):1318-1325.[DOI:10.1016/j.patcog.2011.09.021]
Bucak S S, Jin R, Jain K A. Multiple kernel learning for visual object recognition:a review[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(7):1354-1369.[DOI:10.1109/TPAMI.2013.212]
Liu J, Ji S W, Ye J P. SLEP: sparse learning with efficient projections version 4. 1[EB/OL]. (2011-12-28)[2014-09-24] . http://yelab.net/software/SLEP/manual.pdf http://yelab.net/software/SLEP/manual.pdf .
Shotton J, Johnson M, Cipolla R. Semantic texton forests for image categorization and segmentation[C]//Proceedings of 2008 IEEE Conference on Computer Vision and Pattern Recognition. Anchorage, Alaska, USA: IEEE, 2008: 1-8.[ DOI：10.1109/CVPR.2008.4587503 http://dx.doi.org/10.1109/CVPR.2008.4587503 ]
Gilchrist A, Kossyfidis C, Bonato F, et al. An anchoring theory of lightness perception[J]. Psychological Review, 1999, 106(4):795-834.[DOI:10.1037/0033-295X.106.4.795]
Kumar P, Yildirim E A. Minimum-volume enclosing ellipsoids and core sets[J]. Journal of Optimization Theory and Applications, 2005, 126(1):1-21.[DOI:10.1007/s10957-005-2653-6]
Grigorescu S E, Petkov N, Kruizinga P. Comparison of texture features based on Gabor filters[J]. IEEE Transactions on Image Processing, 2002, 11(10):1160-1167.[DOI:10.1109/TIP.2002.804262]
Condorovici R G, Florea C, Vertan C. Automatically classifying paintings with perceptual inspired descriptors[J]. Journal of Visual Communication and Image Representation, 2015, 26:222-230.[DOI:10.1016/j.jvcir.2014.11.016]
Khan R, Van De Weijer J, Khan F S, et al. Discriminative color descriptors[C] //Proceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition. Portland, Oregon, USA: IEEE, 2013: 2866-2873.[ DOI：10.1109/CVPR.2013.369 http://dx.doi.org/10.1109/CVPR.2013.369 ]
Manjunath B S, Ohm J R, Vasudevan V V, et al. Color and texture descriptors[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2001, 11(6):703-715.[DOI:10.1109/76.927424]
Sheng J C. Automatic categorization of traditional Chinese paintings based on wavelet transform[J]. Computer Science, 2014, 41(2):317-319.
盛家川.基于小波变换的国画特征提取及分类[J].计算机科学, 2014, 41(2):317-319.[DOI:10.3969/j.issn.1002-137X.2014.02.069]
Yang B, Xu D Q, Yang X, et al. Painting image classification based on aesthetic style similarity rule[J]. Journal of Zhejiang University:Engineering Science, 2013, 47(8):1486-1492.
杨冰, 许端清, 杨鑫, 等.基于艺术风格相似性规则的绘画图像分类[J].浙江大学学报:工学版, 2013, 47(8):1486-1492.[DOI:10.3785/j.issn.1008-973X.2013.08.024]
Gehler P, Nowozin S. On feature combination for multiclass object classification[C]//Proceedings of the 12th IEEE International Conference on Computer Vision. Kyoto, Japan: IEEE, 2009: 221-228.[ DOI：10.1109/ICCV.2009.5459169 http://dx.doi.org/10.1109/ICCV.2009.5459169 ]
Zou Q, Cao Y, Li Q Q, et al. Chronological classification of ancient paintings using appearance and shape features[J]. Pattern Recognition Letters, 2014, 49:146-154.[DOI:10.1016/j.patrec.2014.07.002]
Vedaldi A, Fulkerson B. VLFeat: an open and portable library of computer vision algorithms[EB/OL]. [2010-10-25] . http://www.vlfeat.org/ http://www.vlfeat.org/ .
Hinton G E, Osindero S, Teh Y M. A fast learning algorithm for deep belief nets[J]. Neural Computation, 2006, 18(7):1527-1554.[DOI:10.1162/neco.2006.18.7.1527]
Ferrari V, Fevrier L, Jurie F, et al. Groups of adjacent contour segments for object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 30(1):36-51.[DOI:10.1109/TPAMI.2007.1144]
Fan Z B, Sam Zheng X, Zhang K. Computational analysis and eye movement experiments of white space in Chinese paintings[C]//Proceedings of 2015 IEEE International Conference on Progress in Informatics and Computing. Nanjing, China: IEEE, 2015: 301-306.[ DOI：10.1109/PIC.2015.7489858 http://dx.doi.org/10.1109/PIC.2015.7489858 ]
Itti L, Koch C, Niebur E. A model of saliency-based visual attention for rapid scene analysis[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(11):1254-1259.[DOI:10.1109/34.730558]
Wu R, Yan S G, Shan Y, et al. Deep image:scaling up image recognition[J]. Computer Science, 2015. arXiv:1501.02876, 2015.
Ciregan D, Meier U, Schmidhuber J. Multi-column deep neural networks for image classification[C]//Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition. Providence, RI, USA: IEEE, 2012: 3642-3649.[ DOI：10.1109/CVPR.2012.6248110 http://dx.doi.org/10.1109/CVPR.2012.6248110 ]
Zhou Z H. Machine Learning[M]. Beijing:Tsinghua University Press, 2016:24-35.
周志华.机器学习[M].北京:清华大学出版社, 2016:24-35.
Yanulevskaya V, Uijlings J, Bruni E, et al. In the eye of the beholder: employing statistical analysis and eye tracking for analyzing abstract paintings[C]//Proceedings of the 20th ACM international conference on multimedia. Nara, Japan: ACM, 2012: 349-358.[ DOI：10.1145/2393347.2393399 http://dx.doi.org/10.1145/2393347.2393399 ]
Kobayashi S. The aim and method of the color image scale[J]. Color Research Application, 1981, 6(2):93-107.[DOI:10.1002/col.5080060210]
Kobayashi S. Color Image Scale[M]. Tokyo:Kodansha International, 1991.
Nascimento S M C, Linhares J M M, Montagner C, et al. The colors of paintings and viewers' preferences[J]. Vision Research, 2017, 130:76-84.[DOI:10.1016/j.visres.2016.11.006]
Krejtz K, Duchowski A, Szmidt T, et al. Gaze transition entropy[J]. ACM Transactions on Applied Perception, 2015, 13(1):#4.[DOI:10.1145/2834121]
Bao H, Liang Y, Liu H Z, et al. A dataset for research of traditional Chinese painting and calligraphy images[C]//Proceedings of the 2nd International Conference on Information and Multimedia Technology. Singapore: IACSIT Press, 2010: 136-143.[ DOI：10.7763/IPCSIT.2012.V42.20 http://dx.doi.org/10.7763/IPCSIT.2012.V42.20 ]
Sheng J C. An effective approach to identify digitized IWPs (Ink and Wash Paintings)[C]//Proceedings of the 5th International Congress on Image and Signal Processing, Chongqing, China: IEEE, 2012: 407-410.[ DOI：10.1109/CISP.2012.6469719 http://dx.doi.org/10.1109/CISP.2012.6469719 ]
相关文章
相关作者
相关机构
京公网安备11010802024621
