计算机工程 ›› 2013, Vol. 39 ›› Issue (5): 243-247,252.doi: 10.3969/j.issn.1000-3428.2013.05.053

• 图形图像处理 • 上一篇    下一篇

基于复杂网络的图像建模与特征提取方法

汤 进1,2,陈 影2,江 波2,罗 斌1,2   

  1. (1. 安徽省工业图像处理与分析重点实验室,合肥 230039; 2. 安徽大学计算机科学与技术学院,合肥 230601)
  • 收稿日期:2012-03-05 出版日期:2013-05-15 发布日期:2013-05-14
  • 作者简介:汤 进(1976-),男,副教授、博士,主研方向:图像处理,模式识别;陈 影、江 波,硕士研究生;罗 斌,教授
  • 基金项目:
    国家自然科学基金资助项目(61073116, 61003038);安徽省教育厅自然科学基金资助重点项目(KJ2010A006);安徽大学“211工程”创新团队基金资助项目

Image Modeling and Feature Extraction Method Based on Complex Network

TANG Jin 1,2, CHEN Ying 2, JIANG Bo 2, LUO Bin 1,2   

  1. (1. Key Lab of Industrial Image Processing & Analysis of Anhui Province, Hefei 230039, China; 2. School of Computer Science and Technology, Anhui University, Hefei 230601, China)
  • Received:2012-03-05 Online:2013-05-15 Published:2013-05-14

摘要: 针对传统图像结构图表示特征不稳定的问题,提出一种基于复杂网络模型的图像表示与识别方法。以图像的关键点作为网络节点,构建复杂网络初始模型。利用最小生成树分解方法对初始网络模型进行动态演化,提取不同演化阶段下的网络特征,实现对图像结构特征的描述。该方法直接利用图像关键点之间的空间分布信息,结构简单。分类与聚类实验结果表明,与传统基于边权值阈值的演化方法相比,该方法能更准确地描述图像的结构。

关键词: 图像识别, 最小生成树, 动态演化, 特征提取, 小世界网络, 复杂网络

Abstract: For the structure characteristics usually become instable in traditional graph based image representation methods, a novel image representation and recognition method based on complex network is proposed in this paper. Key points are extracted for an image and an initial complex network is constructed in which nodes correspond to the key points. A novel dynamic evolution process is devised for the initial complex network using the minimum spanning tree decomposition. The features of the networks in different evolution stages are extracted to finally achieve image structural information extraction. This method can simply describe an image by using geometrical feature of the image key points. Experimental results on both classification and clustering demonstrate that the proposed method outperforms the traditional edge weight threshold evolution method and it can describe the structure of images more effectively.

Key words: image recognition, minimum spanning tree, dynamic evolution, feature extraction, small-world network, complex network

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