|本期目录/Table of Contents|

[1]廖绮绮,李翠华*.基于支持向量机语义分类的两种图像检索方法[J].厦门大学学报(自然科学版),2010,49(04):487.
 LIAO Qi qi,LI Cui hua*.Two Image Retrieval Methods Based on Support Vector Machines Semantic Classification[J].Journal of Xiamen University(Natural Science),2010,49(04):487.
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基于支持向量机语义分类的两种图像检索方法(PDF)
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《厦门大学学报(自然科学版)》[ISSN:0438-0479/CN:35-1070/N]

卷:
49卷
期数:
2010年04期
页码:
487
栏目:
研究论文
出版日期:
2010-07-20

文章信息/Info

Title:
Two Image Retrieval Methods Based on Support Vector Machines Semantic Classification
作者:
廖绮绮李翠华*
厦门大学信息科学与技术学院,福建 厦门 361005
Author(s):
LIAO QiqiLI Cuihua*
School of Information Science and Technology,Xiamen University,Xiamen 361005,China
关键词:
图像检索语义特征支持向量机分类器
Keywords:
image retrievalsemantic featuresupport vector machines
分类号:
TP 391.41
文献标志码:
-
摘要:
为了更好的解决基于内容的图像检索问题,提出了2种基于语义的图像检索方法.第1种是基于支持向量机(SVM)语义分类的图像检索方法.该方法首先提取训练图像库的底层特征信息,然后利用SVM对所提取的特征进行训练,构造多分类器.在此基础上,利用分类器对测试图像自动分类,得到图像属于各个类别的概率,实现图像检索.第2种是利用图像自动标注方法进行检索.在基于语义的图像自动标注中,先对训练集进行人工标注,对测试图像利用SVM分类器进行分类,并找到与该图像最相似的N张构成图像集,对该图像集的标注进行统计,找到关键词,从而
Abstract:
In order to solve the problem of content based image retrieval(CBIR),two novel methods of image retrieval based on semantic are proposed.Firstly,we use the method of image retrieval which is based on the SVM semantic classification.In this method,we will use Support Vector Machines (SVM) Statistical Learning Theory tools to train the visual image features in order to construct Multiclass Classifier. Thus the test images can be automatically classified by using this classifier,and we will get the probability of the images belong to the every class easily.Then we use this probability to compute the similarity between images.The second method is the image retrieval based on automatic image annotation.Based on the SVM semantic classification method,the image database which is noted will be used as the training image set.Then we use SVM classifier to classify the test images and find the Nnearest similar images in the image library.Then we estimate the probability of the key words from those images and the automatic image annotation will be accomplished.And this image annotation will be used to image retrieval.Experiments conducted on standard dataset and realistic dataset demonstrate the effectiveness and efficiency of the proposed approach for image retrieval.

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备注/Memo

备注/Memo:
收稿日期:20090910基金项目:国家重点基础研究发展计划(973计划)项目(2007CB311005);国家863计划项目(2006AA01Z129);福建省自然科学基金计划资助项目(A0710020)以及厦门大学985二期信息创新平台项目 *通讯作者:chli@xmu.edu.cn
更新日期/Last Update: 2010-07-20