数据场典型相关分析及其在图像分割中的应用

李文平; 杨静; 印桂生; 张健沛

doi:10.16383/j.aas.2015.c130896

数据场典型相关分析及其在图像分割中的应用

doi: 10.16383/j.aas.2015.c130896

李文平^1,2,
杨静^1, ,,
印桂生¹,
张健沛¹

1.
哈尔滨工程大学计算机科学与技术学院哈尔滨 150001;
2.
嘉兴学院数理与信息工程学院嘉兴 314001

基金项目:

国家自然科学基金(61370083,61073043,61073041,61402126),高等学校博士学科点专项科研基金(20112304110011,20122304110012)资助

详细信息

作者简介:
李文平哈尔滨工程大学国家大学科技园博士后,嘉兴学院讲师.主要研究方向为数据挖掘,隐私保护,膜计算.E-mail:liwenping@hrbeu.edu.cn

通讯作者:
杨静哈尔滨工程大学教授.主要研究方向为数据库理论,数据挖掘,隐私保护.本文通信作者.E-mail:yangjing@hrbeu.edu.cn

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出版历程
- 收稿日期: 2013-09-16
- 修回日期: 2014-11-21
- 刊出日期: 2015-04-20

Data Field Based Canonical Correlation Analysis and Its Application to Image Segmentation

1.
College of Computer Science and Technology, Harbin Engineering University, Harbin 150001;
2.
College of Mathematics Physics and Information Engineering, Jiaxing University, Jiaxing 314001

Funds:

Supported by National Natural Science Foundation of China(61370083, 61073043, 61073041, 61402126), and Research Fund for the Doctoral Program of Higher Education of China(20112304110011, 20122304110012)

摘要

摘要: 针对数据场环境下多维数据的低维特征提取问题,本文将数据之间的相互作用纳入其相关性求解中,提出一种基于数据场的典型相关分析(Data field based canonical correlation analysis, DFCCA)方法. DFCCA提取的特征具有良好的分布特性,原空间上相隔较远的数据点对的特征聚集在一个较小区域内,而相邻数据点对的特征却有规律地分布在其他点所聚集区域的周围.此特性使得DFCCA具有较好的边界辨识能力,将其应用于图像分割的实验结果表明, DFCCA提取的复杂图像边界具有较好的保真度.
- 典型相关分析 /
- 数据场 /
- 特征提取 /
- 图像分割
Abstract: In this paper, for extracting low-dimensional features from multi-dimensional data in data field environment, we propose a novel method of canonical correlation analysis(CCA) called DFCCA(data field based CCA) by introducing interactions among data into data correlation solving. The features extracted by DFCCA have better distribution properties, that is the features corresponding to a data point pair that are far apart from each other gather together in a small region, but other features corresponding to the pair of data points that are neighboring each other will scatter regularly around the region. Thanks to these properties, DFCCA has a good capability of frontier identification. Experimental results on image segmentation demonstrate that the frontiers extracted from complex images by DFCCA hold better fidelity.
- Canonical correlation analysis(CCA) /
- data field /
- feature extraction /
- image segmentation

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参考文献(40)

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数据场典型相关分析及其在图像分割中的应用

doi: 10.16383/j.aas.2015.c130896

作者简介:
李文平哈尔滨工程大学国家大学科技园博士后,嘉兴学院讲师.主要研究方向为数据挖掘,隐私保护,膜计算.E-mail:liwenping@hrbeu.edu.cn

通讯作者:
杨静哈尔滨工程大学教授.主要研究方向为数据库理论,数据挖掘,隐私保护.本文通信作者.E-mail:yangjing@hrbeu.edu.cn

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Data Field Based Canonical Correlation Analysis and Its Application to Image Segmentation

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目录

留言板

数据场典型相关分析及其在图像分割中的应用

doi: 10.16383/j.aas.2015.c130896

作者简介: 李文平 哈尔滨工程大学国家大学科技园博士后,嘉兴学院讲师.主要研究方向为数据挖掘,隐私保护,膜计算.E-mail:liwenping@hrbeu.edu.cn

通讯作者: 杨静 哈尔滨工程大学教授.主要研究方向为数据库理论,数据挖掘,隐私保护.本文通信作者.E-mail:yangjing@hrbeu.edu.cn

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出版历程

Data Field Based Canonical Correlation Analysis and Its Application to Image Segmentation

计量

出版历程

目录

作者简介:
李文平哈尔滨工程大学国家大学科技园博士后,嘉兴学院讲师.主要研究方向为数据挖掘,隐私保护,膜计算.E-mail:liwenping@hrbeu.edu.cn

通讯作者:
杨静哈尔滨工程大学教授.主要研究方向为数据库理论,数据挖掘,隐私保护.本文通信作者.E-mail:yangjing@hrbeu.edu.cn