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[1]李海峰,杜军平.颜色特征的图像分类技术研究[J].智能系统学报,2008,3(2):155-158.
　LI Hai-feng,DU Jun-ping.Image classification technology based on color features[J].CAAI Transactions on Intelligent Systems,2008,3(2):155-158.

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颜色特征的图像分类技术研究

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《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷: 3 期数: 2008年第2期页码: 155-158 栏目: 学术论文—人工智能基础出版日期: 2008-04-25

Title:: Image classification technology based on color features

文章编号:: 1673-4785(2008)02-0155-04

作者:: 李海峰¹，杜军平^2; 1. 北京工商大学计算机学院，北京100037；
?2. 北京邮电大学计算机学院，北京100876

Author(s):: LI Hai-feng^1, DU Jun-ping^2; 1. School of Computer Science, Beijing Business and Technology University, Beij ing 100037, China;
2. School of Computer and Technology, Beijing University of Posts and Teleco mmunications, Beijing 100876,China

关键词:: 图像分类; 颜色特征; Boosting算法

Keywords:: image classification; color features; boosting algorithm

分类号:: TP391

文献标志码:: A

摘要:: 研究了基于颜色的图像特征对于图像分类结果的影响.给出了采用基于颜色位置分布特征进行分类的方法，并与基于RGB直方图特征和基于HSV直方图特征的方法进行了比较.分别采用随机森林、Boosting算法和MLP神经网络3种分类方法进行图像分类，建立了自然图像分类系统.基于实验结果比较了随机森林、Boosting算法和MLP神经网络3种分类方法的优缺点，发现Boosting算法表现最好，更加适合于图像分类.

Abstract:: This paper studies the effects of an image’s color features on image classification. We developed a new classification method based on positional distribution of colors and compare it with other methods based on RGB and HSV histograms. The random forest, the Boosting algorithm, and the MLP neural network were applied respectively to classify images and a natural image classification system built up. The advantages and disadvantages of these three classification algorithms are discussed according to experimental results, showing that the boosting algorithm gives the best performance and is more suitable to image classification. 

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备注/Memo

收稿日期：2007-08-07.
基金项目：
国家自然科学基金资助项目（60773112）;
北京市自然科学基金资助项目（4082021）.
作者简介：
李海峰，男，1980年生，硕士研究生,主要研究方向为模式识别、智能信息系统等.
杜军平，女，1963年生，教授，博士生导师，中国人工智能学会常务副秘书长，中国自动化学会智能自动化专业委员会副秘书长，中国旅游信息标准技术委员会主任委员,主要研究方向为数据挖掘、Agent理论与技术、智能信息处理、旅游智能信息系统等.近年来完成科研项目20余项，发表学术论文90余篇，出版著作3部.
通讯作者：杜军平.E-mail：junpingd@bupt.edu.cn.

更新日期/Last Update: 2009-05-11

颜色特征的图像分类技术研究 PDF下载HTML

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颜色特征的图像分类技术研究

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