目的 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 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 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 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 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 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 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 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.