基于内容的图像检索技术研究

基于内容的图像检索技术研究

中国科学技术大学

硕士学位论文

基于内容的图像检索技术研究

姓名：高如如

申请学位级别：硕士

专业：电路与系统

指导教师：郭立;朱俊株

2011-05-13

基于内容的图像检索技术研究

摘 要

多媒体技术的快速发展和海量图像库的不断涌现，让基于内容的图像检索成为备受关注的研究领域之一。如何快速准确地从大型数据库中检索到相关图像已成为急需解决的难题。传统的基于内容的图像检索方法在图像底层特征和高层语义间存在“语义鸿沟”，为了减少这一影响，研究者提出很多改进措施，其中最有效的方法就是利用局部特征表示和引入相关反馈技术。

本文主要工作包括：

1、针对全局特征的不能精确描述图像细节的缺陷以及传统小波显著点阈值固定的问题，提出一种改进的基于显著点的图像检索，本文显著值阈值根据每幅图像所有粗糙显著点的显著值和的百分比确定，并且为避免显著点集中在密集纹理区域内，还对每个显著点5*5邻域内进行检测，如果存在显著点，则保留显著值最大的那个点。显著点特征提取过程中提出将局部特征和全局特征结合的方法，局部特征的提取利用显著点的空间位置分布信息，将显著点划分为3个同心圆环，计算每个圆环的颜色矩和形状不变矩，并与全局纹理特征相结合。实验结果表明，该算法的检索结果满足一定要求。

2、针对提取的显著点缺乏语义信息的问题，提出将显著点和SVM相关反馈结合的图像检索方法。采用的SVM相关反馈是根据用户标记的正负样本训练学习新的分类模型，对特征库重新分类，给出新的检索结果。

3、本文提出一种基于显著点和模糊特征估计索引结合的图像检索方法，模糊特征估计索引是通过反馈的正负样本集数据计算“类间模糊”和“类内模糊”，得到新的特征向量的权值，然后重新计算加权欧式距离，给出检索结果。反馈结果表明，经过反馈后的检索性能得到较大提高。

4、为验证本文提出的算法性能，设计实现一个面向本地数据库的测试系统。该系统可通过小网格对结果进行预览、选择特征组合方式、进行相关反馈以及显示附加信息等。

关键词：基于内容的图像检索 显著点 相关反馈 支持向量机 特征估计索引

基于内容的图像检索技术研究

ABSTRACT

With the rapid development of multimedia technology and the emergence of large amount of images, content-based image retrieval (CBIR) has become one of most important research fields. How to retrieve relevant image from large database rapidly and accurately has become an urgent problem. The traditional CBIR methods have semantic gap between the images’ underlying characteristics and high-level semantic. In order to reduce the influence, researchers put forward a lot of improved methods. The most effective ways are local feature expression and relevance feedback.

Specific works of this paper mainly include:

1. According to the problems of global characteristics can not describe image details accurately and traditional salient points extraction has a fixed threshold, the article puts forward an improved image retrieval based on salient points. The threshold can be set to a given percentage of the sum of all the saliency values. In order to avoid salient points concentrated in dense texture area, we detect 5 * 5 neighborhoods for each salient point and just retain the maximum one if there are salient points. Salient point features are extracted local as well as global ones. The local features utilize space distribution information of salient points. We divide them into three concentric rings and calculate color moments and shape invariant moments in every annular and global texture features. The experimental results show that the algorithm is good for image retrieval.

2. In order to overcome shortcomings that the salient points are lack of semantic information, a method for image retrieval based on salient points and SVM relevance feedback is proposed. SVM relevance feedback trains new classification model from learning the relevant and irrelevant samples labeled by the user. Reclassify the feature library, and give the new retrieval results.

3. Propose a method for image retrieval based on salient points and fuzzy feature evaluation index. Fuzzy feature evaluation index is computed from the ‘intraset ambiguity’ and the ‘inerset ambiguity’ as obtained from the relevant and irrelevant set of images. Then recalculate weighted Euclidean distance and given the new retrieval results. Experiments show that the performances after feedback retrieval both be improved greatly.

4. In order to validate the performance of proposed algorithms, we design and implement a test system for local image database. The system can preview the results

基于内容的图像检索技术研究

through the small grid, choose features, feedback relevant and irrelevant samples, and display the additional information etc.

Key Words: content based image retrieval (CBIR), salient points, relevance feedback (RF), support vector machine (SVM), feature evaluation index(FEI）

基于内容的图像检索技术研究

中国科学技术大学学位论文原创性声明

本人声明所呈交的学位论文,是本人在导师指导下进行研究工作所取得的成果。除已特别加以标注和致谢的地方外，论文中不包含任何他人已经发表或撰写过的研究成果。与我一同工作的同志对本研究所做的贡献均已在论 …… 此处隐藏：10403字，全部文档内容请下载后查看。喜欢就下载吧 ……