计算机理论论文基于自适应特征综合的图像检索技术研究.doc

基于自适应特征综合的图像检索技术研究 基于自适应特征综合的图像检索技术研究 adaptive feature integration image retrieval research 【中文摘要】 随着internet的普及和发展,图像数据飞速膨胀,如何高效、快速的检索到所需要的图像数据成为当前图像应用领域的一个研究热点。为了便于图像的检索和识别,基于内容的图像检索技术应运而生。本文在分析现有的基于内容的检索技术的基础上,针对目前技术中存在对图像特征提取不够有效和准确性方面的局限,主要进行了如下研究:首先介绍了图像检索技术研究的发展历程以及技术现状,然后分别讨论和研究了基于颜色、纹理、形状单一特征的图像检索技术。其中为了弥补颜色直方图所丢失的颜色空间信息,改进了一种分块直方图的颜色特征提取方法,并通过实验对比全局直方图检索结果,证明了分块直方图的算法要更优。基于单一特征的方法只能表达图像的部分属性,对图像内容的描述片面,缺足够的区分信息,本文通过实验研究了基于静态特征组合的图像检索算法,通过对实验结果的分析,总结了算法的优缺点,并提出了静态特征组合所存在对特征组合机械的缺点。根据前面的分析,提出了一种基于自适应特征综合的图像检索算法,该算法可以通过图像本身底层特征结构来自适应地选取各个特征在计算综合相似度距离中的权重,以达到在没有用户干预的情况下,达到较好的检索效果。通过实验对比 / ,该方法相对于前面的静态综合特征图像检索方法具有更好的检索效果。最后总结了本文的工作和目前算法仍存在的问题,并讨论了基于内容图像检索今后的发展方向【英文摘要】 with the popularization and development of internet, the number of image data grows dramatically fast, and that how to retrieve image efficiently and quickly becomes an important issue in the field of images application. in order to manage and retrieve large amounts of images, the cbir has emerged to be one of the hot research areas in image process domain.based on the analysis of the cbir systems in existence, for the problems that the actual cbir systems can not distill effectual and accurate feature combination , the dissertation do some researches as follows:firstly the history and development of cbir are introduced, and the algorithms of cbir which based color, texture, and shape feature are discussed respectively. for redeeming the missing space information, a new method of partition color histogram is presented, and the paper compared the algorithm with the traditional color histogram by experimentation. experimental results have shown that the partition color histogram method / has better performance.the method which based on single feature can only express part of image information, only describe exparte content of the image and lack of distinct information, a method image retrieval method based color, texture and shape feature is researched and the method is compared with the based on single feature method by doing research. after studying the results, the dissertation summarized merits and shortcomings of the method and suggestions for improvement are put forward. by discussing the shortcomings of the static feature selected image retrieve method, a adaptive feature selection image retrieval method is presented. based on the analysis of the image features, this method can adjust the weights of the feature in feature combination, in order to improve the effect of the cbir without users helping. a large number of experiments show that the adaptive feature integration method is better than the tradition static feature selected image retrieve method.finally, the work of this paper is summarized, and the forecast of the development of content-based image retrieval is discussed.【中文关键词】 自适应特征综合; 图像检索; 分块直方图; 特征权重 【英文关键词】 image retrieval; adaptive feature selection; divided histogram ; feature weights 基于自适应特征综合的图像检索技术研究摘要 3-4 abstract 4-5 第一章 绪论 9-18 1.1 基于内容图像检索研究背景与意义 9-10 1.2 基于内容的图像检索方法综述 10-16 1.2.1 基于内容的图像检索概述 10-12 1.2.2 基于内容的图像检索技术研究现状 12-15 1.2.3 基于内容的图像检索现存问题 15-16 1.3 本文所做工作及结构安排 16-18 1.3.1 本文所做工作 16-17 1.3.2 本文的结构安排 17-18 第二章 基于颜色特征的图像检索 18-33 2.1 颜色特征概述 18-23 2.1.1 颜色空间 18-21 2.1.2 颜色特征的表达 21-23 2.1.3 颜色匹配算法 23 2.1.4 颜色量化 23 2.2 基于hsv 空间颜色特征的图像检索 23-25 2.3 基于全局颜色直方图的算法实验 25-27 2.4 基于分块直方图的图像检索 27-30 2.5 基于分块直方图的算法实验 30-32 2.6 小结 32-33 第三章 基于纹理特征的图像检索 33-41 3.1 纹理特征概述 33-34 3.2 基于灰度共生矩阵纹理特征提取算法 34-37 3.2.1 计算共生矩阵 34-36 3.2.2 提取纹理特征 36-37 3.3 纹理特征的算法实验 37-40 3.4 小结 40-41 第四章 基于形状特征的图像检索 41-47 4.1 形状特征概述 41-42 4.2 基于hu 七个不变矩的形状特征提取算法 42-44 4.3 基于hu 七个不变矩的算法实验 44-46 4.4 小结 46-47 第五章 基于自适应特征综合的图像检索 47-67 5.1 静态特征综合表示方法 47-51 5.1.1 静态特征综合检索算法 47-48 5.1.2 实验结果与分析 48-51 5.2 自适应特征综合算法提出背景 51-53 5.3 自适应特征综合算法概述 53-56 5.4 基