• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

计算机工程与科学

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基于近邻传递与SC特征的MEAP传统云纹图案自动分类

江明1,陈雷雷2,葛洪伟2,苏树智2   

  1. (1.江南大学设计学院,无锡 江苏 214122;2.江南大学物联网工程学院,无锡 江苏 214122)
  • 收稿日期:2015-09-14 修回日期:2016-03-29 出版日期:2017-06-25 发布日期:2017-06-25
  • 基金资助:

    国家自然科学基金(61305017);江苏省普通高校研究生科研创新计划(KYLX15_1169);江苏高校优势学科建设工程资助项目

Neighbor propagation and shape context based
MEAP for moire images automatic classification
 

JIANG Ming1,CHEN Lei-lei2,GE Hong-wei2,SU Shu-zhi2   

  1. (1.School of Design,Jiangnan University,Wuxi 214122;
    2.School of Internet of Things,Jiangnan University,Wuxi 214122,China)
  • Received:2015-09-14 Revised:2016-03-29 Online:2017-06-25 Published:2017-06-25

摘要:

云纹是我国古代装饰纹样中独具魅力的瑰宝,卷云纹是其中重要的一支,不仅具有很高的艺术价值,对当代的艺术设计实践也有着深远的启示作用。因此,对其进行归类分析从而发现云纹图案中蕴含的艺术思想、造型手法,无论是对于文化艺术研究还是对于聚类算法研究都具有重要意义。针对云纹图案变化繁复、人工分类效率低下的问题,提出一种基于自适应阈值近邻关系传递的多子类中心近邻传播聚类算法(ANP-MEAP),结合形状上下文特征(SC)提取算法对云纹图案的自动分类进行了有益的尝试。实验显示了结合SC特征的ANP-MEAP算法进行云纹图案自动分类的可行性和优越性。本文提出的云纹图案聚类算法,对于其他传统艺术图案的聚类分析也具有很好的借鉴意义。

关键词: 多子类中心近邻传播聚类, 形状上下文, 相似性度量, 近邻传递

Abstract:

Moire is a unique treasure of Chinese ancient decorative patterns. Curved moire images are an important branch which not only has very high artistic value, but also plays a far-reaching role in the enlightenment on contemporary art design practice. Therefore classifying and analyzing curved moire images to find the artistic connotations and modeling techniques has great significance in art design and clustering research. Moire patterns are changeful and complicated, so the manual classification of moire patterns is very inefficient. In order to solve this problem, we propose an adaptive threshold neighbor propagation based multi-exemplar affinity propagation algorithm (ANP-MEAP), which  combines  shape context features to classify moire patterns automatically. Experimental results verify the feasibility and superiority of the automatic clustering algorithm. Moreover, it also has certain significance for the clustering and analysis of other traditional art patterns.
 

Key words: multi-exemplar affinity propagation, shape context, similarity measure, neighbor propagation