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基于内容的图像数据库语义分类相关技术研究
其他题名Study on Content-based Semantic Classification of Image Database
王艳妮
学位类型工学博士
导师胡包钢
2003-08-01
学位授予单位中国科学院研究生院
学位授予地点中国科学院自动化研究所
学位专业模式识别与智能系统
关键词图像分类 图像检索 Svm 特征提取与选择 Google图像检索 Image Classification Image Retrieval Svm Feature Extraction And Selection Google Image Retrieval
摘要随着互联网和计算机硬件的迅猛发展,图像信息的产生、存储、传输以及 访问数量呈指数级增长,对图像信息的检索给传统的检索方式提出了挑战。采 用低层特征描述的图像检索系统还不能满足用户的检索需求,本文提出的采用 语义信息组织图像数据库的方式是实现图像有效检索的基础。 本文主要针对图像语义分类和检索进行研究,论文的主要工作归纳如下: (1) 针对各种图像检索系统和图像语义分类进行了综述,系统地分析了基 于内容的图像检索中所用的各种特征,并对各个特征的优缺点以及适用范围进 行了比较分析,对于后续章节中图像特征选择和新特征的提取提供了借鉴。 (2) 提出了一种基于支撑向量机(SVM)的分等级图像语义分类系统。针 对室内与室外图像分类的特点,提出了一种结合目标物体的语义特征;提出了 一种区别建筑物与风景图像的中层语义特征——边缘直线束特征。实验结果表 明采用这两个特征实现的分类性能要优于其它特征。将单类SVM分类器应用于 分类系统中解决了样本不充分的训练问题。 (3) 提出了图像语义分类系统的改进方案,根据不同特征的推广误差来确 定各自的权值,分别采用主成分分析(PCA)、线性判别分析(FL,DA)、多元尺 度分析(MDS)对图像特征进行降维;通过学习的方式实现了一种基于SvM概 率输出的拒绝机制;提出了增量学习SVM方案来解决新增样本和大量样本的训 练问题;分析比较不同的分类器,通过结合多个SVM分类器来提高系统的分类 能力,分别通过实验验证了以上改进方案的有效性。 (4) 提出了一种基于图像数据库语义索引和相似性学习的图像检索方案, 根据查询图像的语义信息来确定相应的检索图像库,缩小检索的范围进而提高 系统的查准率和检索效率;结合用户的相关反馈信息,采用回归支撑向量(SVR) 来学习用户的检索习惯,从而在检索中预测图像之间的相似性。 (5) 本文将语义分类与Google的图像检索相结合,提出了一种改进 Google图像检索的过滤系统。实验结果表明加入过滤系统可以在一定程度上提 高检索的查准率,并为以后其它语义信息的检索提出了一种有效的解决方案。
其他摘要With the development of Internet and the computer hardware, more and more images are generated, stored and transmitted. There is a great challenge to the traditional image retrieval and management system. The content-based image retrieval system cannot meet the user's demand due to the low-level features used. To retrieval the image effectively, the high-level information should be embedded into the image database. This thesis is mainly focusing on the image retrieval and semantic classification of the image database, and the main contributions are as follows: (1) Based on a brief review of the existing retrieval systems and image semantic classification systems, the low-level features used in the image retrieval system are analyzed. Their advantages and disadvantages of each feature and their application situation are compared. That wilt help users to realize the image feature selection and extraction of new feature. (2) We developed an Support Vector Machine(SVM)-based hierarchical image semantic classification system. A new image feature based on the objects of the image is presented according to the characteristic of the indoor and outdoor images. In addition, the new feature called parallel edge line feature is also suggested for the classification of building and landscape image. The standard two-class SVM, one-class SVM and multi-class SVM are used in different stage of the classification system and achieve a good experimented result. Experiment results show the feasibility and validity of the features and the classifiers. (3) We presented several approaches to improve the system accuracy, which include the dimension reduction of the image features and the local semantic features, rejection option based on the probabilistic output, incremental learning based on the SVM and the combination of different classifiers. Several experiment results confirm the effectiveness of the improved system. (4) In this thesis, image retrieval systems are presented based on the high-level semantic index and similarity learning of the user's feedback. Several machine learning methods are used in predicting the user's similarity habit and the experimental result show the applicability of the retrieval system. (5) An image filter system based on the Google image search engine is presented to overcome the low accuracy of the Internet image retrieval. This system is realized through combining the semantic extraction with the retrieval system, which is a combination of the keyword based and visual feature based image retrieval system. The experiment results show that the accuracy can be significantly improved through introducing the filter system. This model of image filter system provides a good solution to apply the existing searching engine but with improved retrieval accuracy.
馆藏号XWLW831
其他标识符831
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/5782
专题毕业生_博士学位论文
推荐引用方式
GB/T 7714
王艳妮. 基于内容的图像数据库语义分类相关技术研究[D]. 中国科学院自动化研究所. 中国科学院研究生院,2003.
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