[1]陈小娥,陈昭炯.多类SVM在图像艺术属性分类中的应用研究[J].智能系统学报,2009,(02):157-162.
 CHEN Xiao-e,CHEN Zhao-jiong.An application of multiclass SVM in the classification of artistic attributes of images[J].CAAI Transactions on Intelligent Systems,2009,(02):157-162.
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多类SVM在图像艺术属性分类中的应用研究(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
期数:
2009年02期
页码:
157-162
栏目:
出版日期:
2009-04-25

文章信息/Info

Title:
An application of multiclass SVM in the classification of artistic attributes of images
文章编号:
1673-4785(2009)02-0157-06
作者:
陈小娥陈昭炯
福州大学数学与计算机科学学院,福建福州350108
Author(s):
CHEN Xiao-e CHEN Zhao-jiong
College of Mathematics and Computer Science, Fuzhou University, Fujian 350108,China
关键词:
支持向量机二叉树多类分类算法图像艺术属性
Keywords:
SVM multiclass classification algorithm based on binary tree image artistic attributes
分类号:
TP391
文献标志码:
A
摘要:
针对当前图像分类研究中,依据图像艺术风格属性进行分类的算法尚不多见的情况,实现了一种基于艺术属性的图像自动分类系统,其中主要涉及摄影作品、国画、水彩画、素描、油画等几种典型艺术风格的图像.系统采用支持向量机(SVM)作为分类器,运用分等级的分类方法,提出了一种针对艺术属性图像分类的特定SVM二叉树多类分类算法;而后通过对各类图像艺术风格特征的分析,分别提取了有代表性的、区分度好且易于计算的特征;最后针对各级分类特性和分类器总体特性进行了实验分析,实验结果表明,系统具有良好的分类性能. 
Abstract:
In image classification, few current classification algorithms classify images by their artistic attributes. An automatic image classification system based on artistic attributes was developed for classifying images in typical artistic styles such as photographs, Chinese paintings, watercolors, sketches, oil paintings, and so on. The system employed a support vector machine (SVM) as a classifier. Using a classification method at various levels, an SVM binary tree multiclass classification algorithm for image classification with respect to different artistic attributes was proposed. By analyzing the images with respect to the different artistic styles, some easily computed representative characteristics with good discriminability were extracted at each classification level. Experiments on a variety of characteristics at various levels and the total characteristics of classifiers were designed to evaluate the proposed classifier. Experimental results showed that the system has good classification performance.

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备注/Memo

备注/Memo:
收稿日期:2008-05-12.
基金项目:福建省自然科学基金资助项目(A0710006)
作者简介:
陈小娥,女,1984年生,硕士研究生,主要研究方向为图形图像处理技术
陈昭炯, 女,1964年出生,教授,主要研究方向为图形图像处理与智能算法设计等.主持及参与多项国家和省级基金项目,已发表学术论文50余篇. 
通信作者:陈昭炯. E-mail:chenzj@fzu.edu.cn.
更新日期/Last Update: 2009-05-04