CASIA OpenIR  > 毕业生  > 硕士学位论文
Alternative TitleImage Retrieval Based on Low-level Features: Theory, Analysis and Implementation
Thesis Advisor马颂德
Degree Grantor中国科学院研究生院
Place of Conferral中国科学院自动化研究所
Degree Discipline模式识别与智能系统
Abstract基于内容的图像检索近年来一直是一个研究热点。在它的技术推动下,出 现了一些图像数据库管理工具,这样许多多媒体应用技术如数字图书馆等的发 展才成为可能。本论文将讨论这个领域内的许多基本概念和研究课题。我们将 着重讨论一些图像底层特征如颜色、纹理和形状的定义表达、提取算法和匹配 规则,并且提出一个以颜色信息为主导的图像检索方案。此外还会以我们的系 统为实例,讨论图像检索系统的具体实现过程,以及系统实现中需要用到的技 术和可能遇到的问题。 特征表达和提取是图像检索中最基本的问题。颜色、纹理和形状都是一些 可视化底层特征,尽管它们还不足以成为描述图像内容的语义特征,不可能达 到文本描述那样的抽象层次,但它们多少反映了一些原始图像的内容信息,从 而得到了研究者们的极大重视。本论文详细回顾和讨论了这些底层特征的表达 和提取算法,对于特征问的匹配规则也作了深入的分析。 颜色信息是被广泛应用的底层可视化特征,它在拉伸、旋转、透视变换和 有遮挡的情况下都能保持鲁棒的特性。本文中我们提出了一个有效的利用颜色 信息来进行图像检索的方案。这个方案充分利用了颜色空间分布的信息,并且 对传统的聚类过程进行了修正,使得结果更加合理。实验结果表明这个方案是 十分有效和准确的。 自上世纪九十年代以来,企业界和学术界都建立了许多图像检索系统。我 们选择介绍了其中几个最有代表性的系统,并给出了它们最有特色的地方。本 论文还以我们自己的图像检索系统为实例,详细说明了整个系统的架构和信息 流程。
Other AbstractContent-based Image Retrieval has been drawing more and more research attention in the recent years. This is in response to the requirement in multimedia applications, such as digital libraries, where efficient image indexing and access tools are essential. In this thesis, we discuss most of the basic research issues and concepts in this field. We focus our discussions mainly on the definitions, representation, extraction and matching algorithms of low-level image features, including color, texture and shape. An efficient color-induced retrieval scheme is presented, followed by convincing test results. The technical implementation of our image retrieval system, as well as some key components in building such a practical system, is also covered in great detail. Feature representation and extraction are the basis of Content-based Image Retrieval. Unlike text-based features, visual features are the natural way to represent the content of the image, and can be generated automatically. The most frequently used features are color, texture and shape. This thesis provides a comprehensive survey of the representation and extraction of these low-level features, and also pays close attention to the matching criteria between a pair of features. Color information is the most intensively used visual feature in virtue of its strong correlation with the underlying image objects or scenes. Compared to other low-level visual information, color is more robust with respect to scaling, orientation, perspective and occlusion of images. Here we propose an efficient scheme of color-induced image retrieval, which consists of a new set of features and its specific matching strategy. We make use of the spatial knowledge of color distribution pattern and assume a rectifying process to refine the result from traditional color clustering. Experiment results show that our approach is more robust than conventional color clustering methods in terms of accuracy and efficiency. Since the 1990's many Image Retrieval systems have been built, both commercial and academic. We select a few representative systems and highlight their distinct characteristics. In this thesis, we also give a detailed picture of our own Image Retrieval system. The whole system architecture is illustrated thoroughly and the exact operating principle is explained accordingly.
Other Identifier621
Document Type学位论文
Recommended Citation
GB/T 7714
叶枫冀. 基于底层特征的图像检索—理论,分析与实现[D]. 中国科学院自动化研究所. 中国科学院研究生院,2001.
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