个性化图像美学评价的研究进展与趋势  

The review of personalized image aesthetics assessment

作　　者：祝汉城 周勇[1,2] 李雷达[3] 赵佳琦 杜文亮 Zhu Hancheng;Zhou Yong;Li Leida;Zhao Jiaqi;Du Wenliang(School of Computer Science and Technology,China University of Mining and Technology,Xuzhou 221116,China;Engineering Research Center of Mine Digitization of Ministry of Education,Xuzhou 221116,China;School of Artificial Intelligence,Xidian University,Xi′an 710071,China)

机构地区：[1]中国矿业大学计算机科学与技术学院,徐州221116 [2]矿山数字化教育部工程研究中心,徐州221116 [3]西安电子科技大学人工智能学院,西安71007

出　　处：《中国图象图形学报》2022年第10期2937-2951,共15页Journal of Image and Graphics

基　　金：国家自然科学基金项目(62101555,62171340,62002360);江苏省自然科学基金项目(BK20210488,BK20201346,BK20181354);中国博士后科学基金面上项目(2022M713379);中央高校基本科研业务费专项基金资助项目(2021QN1071);江苏省六大高峰人才基金项目(2015-DZXX-010)。

摘　　要：图像美学评价方法是当前研究的热点问题。图像美学评价分为大众化和个性化两种。大众化图像美学评价主要研究大多数人对图像共同的审美感知评估,而个性化图像美学评价可以针对用户的个性化审美感知进行评估。现有的研究工作主要集中在大众化图像美学评价上,但是由于人们对图像的审美体验具有高度主观性,研究针对特定用户的个性化图像美学评价方法更加符合现实意义。目前研究人员针对个性化图像美学评价展开了相关研究,并取得了一定的研究进展。但是现有的文献中缺少对个性化图像美学评价方法的综述,本文针对个性化图像美学评价的研究进展与趋势进行概述。首先分析图像美学评价的研究现状与发展趋势;然后针对现阶段的个性化图像美学评价模型进行概述,将现有的个性化图像美学评价模型总结为基于协同过滤的模型、基于用户交互的模型和基于审美差异的模型,并分析这3类模型主要的设计思路以及优缺点;最后介绍个性化图像美学评价在精准营销、个性化推荐系统、个性化视觉增强和个性化艺术设计上的应用前景,并指出未来研究工作在主观特性分析和知识驱动建模等方面的发展方向。The multimedia imaging technology can meet people’s visual demands to a certain extent.People can easily obtain high-quality images through mobile devices,so people begin to pay more attention to their aesthetic experience of images,which makes the image aesthetics assessment(IAA)method become a hotspot issue and frontier technology in the current image processing and computer vision fields.Intelligent IAA can be developed to imitate people’s aesthetic perception of images and predict the results of aesthetic evaluation automatically.Aesthetic-preference images can be screened out.Consequently,IAA is critical to be applied in photography,beauty,photo album management,interface design,and marketing.Generally,IAA can be classified into two categories,including generic image aesthetics assessment(GIAA)and personalized image aesthetics assessment(PIAA).Early researches believe that people have a consensus on the aesthetic experience of images,and leverage the general photography rules to describe most people’s visual aesthetics on images,which are usually affected by many factors,such as light intensity,color richness,and composition.Most of the current GIAA model can predict most people’s aesthetic evaluation results of images.GIAA models can be divided into three aesthetic-related tasks like classification,score regression and distribution prediction.The aesthetic classification task is focused on dividing the image into two classes of“high”and“low”according to the aesthetic experience of most people.The research goal of the aesthetic score regression task can predict the aesthetic score of an image.This task leverages the average aesthetic ratings of most people as the image aesthetic score for regression modeling.However,the two tasks shown above need to convert different people’s aesthetic ratings of images into a unified result(“high”or“low”and score).Label uncertainty is derived from people’s aesthetic experience of images,which makes it difficult for the consensus result.Therefore,

关 键 词：个性化图像美学 评价方法 审美体验 主观特性 知识驱动 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]