Computer Engineering and Applications ›› 2011, Vol. 47 ›› Issue (1): 18-21.DOI: 10.3778/j.issn.1002-8331.2011.01.006

• 博士论坛 • Previous Articles     Next Articles

Fisher information fusion of random color histograms and its applications

ZOU Jian1,2,LIU Chuancai1,ZHANG Yue1,2,LU Guifu1,2   

  1. 1.School of Computer Science and Technology,Nanjing University of Science and Technology,Nanjing 210094,China
    2.School of Mathematics and Physics,Anhui Polytechnic University,Wuhu,Anhui 241000,China
  • Received:2010-09-14 Revised:2010-11-26 Online:2011-01-01 Published:2011-01-01
  • Contact: ZOU Jian

随机颜色直方图的Fisher信息融合及应用

邹 健1,2,刘传才1,张 玥1,2,卢桂馥1,2   

  1. 1.南京理工大学 计算机学院,南京 210094
    2.安徽工程大学 应用数理学院,安徽 芜湖 241000
  • 通讯作者: 邹 健

Abstract: The random partition scheme on color space is adopted to extract normalized random color histograms,thereby realizing the extraction of color distribution information of image in dynamic manner.Based on multinomial Fisher geometry,the averaged extended Fisher information distances are proposed for quantifying overall information divergence between sets of random color histograms.Besides providing the most straightforward elements for k-Nearest Neighbor(kNN) based image classification,the measured information divergences also make it possible to learn the collection of sets of random color histograms on the closure of a product simplex.Original color-based tasks such as classification,clustering and visualization on image set can be then implemented on the reduced set.The experimental results on partial images of ALOI database show the effectiveness of proposed method.

Key words: random color histograms, multinomial geometry, information fusion, manifold learning

摘要: 采用在颜色空间的随机划分基础上提取随机直方图从而实现一种以动态方式提取图像颜色分布信息的方法。基于多项费歇尔几何,采用平均延拓的费歇尔信息距离量化随机颜色直方图集合之间综合的信息差异。除了为基于k-近邻的图像分类提供最直接判别元素外,融合的平均信息差异也使得在一个乘积多项流形闭包上学习随机颜色直方图集的集合成为可能。基于颜色特征的图像分类,聚类及可视化等任务此时可在减少集上完成。在ALOI数据集的部分图像集上获得的实验结果证实了方法的有效性。

关键词: 随机颜色直方图, 多项几何, 信息融合, 流形学习

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