【作者】 乃学尚；

【导师】 田玉敏；

【作者基本信息】 西安电子科技大学 ， 计算机系统结构， 2002， 硕士

【摘要】 本文主要用整数小波变换研究基于内容的图象检索技术，该算法的优点在于：整数小波变换的输入输出都是整数，适合数字图象处理；使用小波变换可以用多分辨率分析提取图象特征，可以降低特征量的维数。 本文首先介绍了小波变换的基本知识，上升型方案和整数小波变换。其次用整数小波变换对彩色图象进行多分辨率分析；由于小波变换的低频部分保持了图象的概貌，因此用小波变换低频部分的局部区域能量和F-范数作为彩色图象的特征向量，对图象进行检索，可以降低特征量的维数，提高检索速度。此外，由于纹理图象的主要特征表现在细节部分，而高频部分的小波系数体现了图象的细节，所以从这些小波系数中提取的特征，能够表征纹理图象的主要特性；实验结果表明，用该方法检索纹理图象，能够达到较好的检索效果，并且对亮度不敏感，这一特点是传统的纹理分析方法难以达到的。 图象匹配算法中使用了比值相似度定义，这种相似度计算简单，易于实现，而且能够获得较好的检索结果。 还实现了基于颜色矩的彩色图象检索算法，基于区域不变矩的二值目标检索算法，提出了一种CBIR系统模型，介绍了常用的CBIR系统评价标准。最后概述了CBIR有待于继续研究的相关领域。更多还原

【Abstract】 The content-based image retrieval algorithm using integer-to-integer WT is studied in this paper. The integer-to-integer WT has the following advantages: it fits for image processing because its input and output values are all integers; it extracts image features by using multi- resolution analysis; and it decreases the size of dimensions of the image features.In this paper, the basic knowledge on WT, lifting-scheme and integer-to-integer WT is briefly introduced first, and then the color image is analyzed by integer-to-integer WT multi-resolution in order to reduce the dimensions of the image. Since the low pass part of WT preserves the sketch of the image, better results of the index of color images can be obtained by taking the local region energy and F-norm of WT as the color image features Furthermore, WT coefficients are used to analyze the texture image. As the main features of the texture image are present in the details and the high pass of the WT coefficients denotes the details of the image, the character deduced from WT coefficients can be used to retrieve the texture image.The degree of similarity with ratio in image matching algorithms is defined, which is single to calculate, easy to realize and effective to get better retrieval results.This paper also realizes color image retrieval and binary-target indexing algorithms based on moments; proposes a CBER framework model; introduces general criterions for CBIR system; and finally summarizes the future research direction of CBIR.更多还原