基于数字报历史优秀版面的样式智能生成与微调  

Intelligent generation and fine tuning of style based on the historical excellent layouts of digital newspapers

作　　者：陶颖 程雨夏 曾振宇 庄跃辉 张艺馨 何兴臻 TAO Ying;CHENG Yuxia;ZENG Zhenyu;ZHUANG Yuehui;ZHANG Yixin;HE Xingzhen(Computer College,Hangzhou Dianzi University,Hangzhou 310018,China;Zhejiang Fangzheng Media Technology Research Institute,Jinhua 321099,China)

机构地区：[1]杭州电子科技大学计算机学院,浙江杭州310018 [2]浙江方正传媒技术研究院,浙江金华321099

出　　处：《智能系统学报》2024年第4期930-940,共11页CAAI Transactions on Intelligent Systems

基　　金：国家自然科学基金项目(61802096);浙江省自然科学基金项目(LY20F020014).

摘　　要：在传统报纸印刷行业中,设计人员需要根据设计规则进行人工排版,其排版过程造价成本较高且耗时耗力。为提高排版效率,研究提出一种基于历史优秀版面的样式自动生成与微调方法。为了从数据中学习到报纸排版的风格,创建一个包含丰富的设计元素参数信息的电子报数据库,这些特征信息能够有效地反映报纸的布局。对于给定的新闻文章,首先根据历史优秀版面训练概率模型来推断电子报版面的样式,并结合固定约束和用户约束保证样式有效,同时构建美学设计原理的量化方法进一步实现样式微调。最后通过定性和定量评估,表明新方法可以生成满足视觉美观性、层次性和可读性的报纸。本文方法可为版面设计样式智能生成提供参考。In traditional newspaper printing industry,designers need to manually typeset according to the design rules.The typesetting process is costly,time-consuming and labor-consuming.In order to improve the efficiency of typesetting,a method of automatic style generation and fine-tuning based on historical excellent layouts is proposed.In order to learn the style of newspaper typesetting from the data,an electronic newspaper database containing rich parameter information of design elements is created,which can effectively reflect the layout of newspapers.For a given news article,firstly,a probability model is trained according to the historical excellent layout to infer the layout style of the electronic newspaper,and the fixed constraints and user constraints are combined to ensure that the style is effective.At the same time,some aesthetic design principles are quantified to further realize style fine-tuning.Finally,through qualitative and quantitative evaluation,it shows that the new method can generate newspapers that meet the visual aesthetics,hierarchy and readability.This method can provide a reference for intelligent generation of layout design styles.

关 键 词：布局自动化 图形设计 设计原理 图像数据库 数据驱动方法 概率分布 聚类 约束规划 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]