【作者】 周金财；

【导师】 洪春勇；

【作者基本信息】 南昌大学 ， 计算机软件与理论， 2007， 硕士

【摘要】 计算机技术、多媒体技术以及Internet技术的长足发展导致大量图像的出现，如何有效地、快速地从大规模的图像数据库中检索出需要的图像是目前一个相当重要而又富有挑战性的研究课题。基于内容的图像检索技术的研究正是为了解决利用自动获取的图像特征，从图像数据库中检索出相关图像的问题。近年来，此项技术的研究非常活跃，在许多领域都有应用。本论文围绕基于内容图像检索中的一些关键技术，包括构建图像数据库、静态非线性综合多特征技术，进行了一些探索性的研究，研究的内容属于目前图象处理和信息检索领域的研究重点，具有相当重要的理论意义和实际应用价值。本文的主要工作总结如下：(1)构建XMLIDB图像数据库。使用面向对象的建模方法，将图像模型化为由几个元对象构成。又将图像数据库模型化为超媒体网络，网络中的节点由图像形成，网络中的链即为图像间的关系。这样的两层结构模式，有利于对图像特征进行扩展和增加对语义的支持。(2)建立图像联系数特征。将集对论中表示确定性与不确定性的量即联系数引入图像检索中，把图像象素分为三类：主色集(同类)、边界颜色集(异类)、噪声颜色集(反类)，由这三类来提供图像信息。继而提出图像的联系数模型，在此模型基础上利用图像的全局形状和局部颜色统计量构成图像联系数向量。(3)基于联系数的图像检索方法。采用图像联系数向量作为图像索引，在形状白适应逼近表示方法的基础上，提出了利用联系数特征向量进行图像检索的检索算法。实验结果表明，该算法具有良好的平移、尺度、旋转不变性，证明联系数是一个有效的图像特征。更多还原

【Abstract】 Due to the steady growth of computer, multimedia, and Internet techniques, a huge amount of images are available. Currently, rapid and effective searching for desired images from large-scale image databases becomes an important and challenging research topic. Content-based image retrieval (CBIR) is the set of techniques to address the problem of retrieving relevant images from an image database based on automatically derived image features.In this dissertation, lots of exploratory research work has been done around some key techniques of CBIR, which include creating image database, technique of multi-feature assembled statically and non-linearly. The presented study is the current research focus of image processing and information retrieval. Thus, the research has both the theory and the application value.The main works of this dissertation are summarized as follows:Firstly, create the image database named XMLIDB. Object-oriented modeling approach is used to translate image into some meta-objects, and image database is modeled a hypermedia-net, in which nodes are images and links are the relation between images. The two-layers architecture is conductive to the expansion of the image feature and to increase the support of semantics.Secondly, build an image feature named Connection number. The method classifies the image into three set: domain color set (same set), border color set (indefinite set) and noise color set (contrary set). An image connection number model is proposed. Based on the model, the statistic of global shape and local color is used to form the connection number vector of image, with which we can query the image database.Thirdly, image retrieval method based-connection number is advanced. The image retrieval algorithm is developed basing on connection number vector. It is shown through performance experiments that this algorithm is of scale invariance and rotation invariance to some extent.更多还原