【作者】 罗晓萍；

【导师】 蒋加伏；

【作者基本信息】 长沙理工大学 ， 计算机应用技术， 2005， 硕士

【摘要】 随着计算机技术、多媒体技术的迅速发展以及Internet 的不断扩大,图像信息变得越来越丰富,如何快速地找到需要的图像成为亟待解决的问题。基于内容的图像检索技术旨在搜索出满足人们主观要求的图像,因此得到了广泛研究。模糊集理论能够促进基于内容的图像检索技术发展,使图像检索技术脱离精确的计算,更符合人类的模糊思维。论文着眼于这一点,讨论了融合模糊集理论的图像检索技术,分析比较了模糊聚类算法FCM 和人工免疫网络aiNet 聚类方法各自的特性,最后提出了基于区域模糊特征的图像检索的改进方法,使原有方法在速度和命中准确率上都得到提高,实现的系统也证实了这一点。论文主要作了以下工作: ¨¨系统地总结了基于内容图像检索的几方面关键技术,包括:低层视觉特征的提取算法、图像特征数据库的索引机制、相似性度量方法、图像检索查询方式、相关性反馈技术和图像检索算法性能的评价策略。¨¨讨论了这几种模糊特征提取技术:简单的颜色模糊直方图、基于颜色隶属模型的直方图、颜色和纹理综合模糊直方图方法、模糊形状表达技术。¨¨分析比较了模糊C 均值聚类算法FCM 和人工免疫网络aiNet 聚类算法的特性,通过它们对相对集中数据、稀疏分散数据、环状和螺旋分布数据聚类来进行比较,得到当遇到潜在的类或簇背离超球面结构时,FCM 算法表现不佳,而aiNet 可以很好的发现数据的内在特征,减少数据中的冗余、描述数据结构和聚类形状,表现良好的适应性。¨¨提出并且实现了基于区域模糊特征的图像检索的改进方法。有三点改进:采用量化再累计的方法来减少参与聚类运算的数据,而不影响聚类结果;使用边界矩来表征区域的形状特征;使用邻接表表征区域空间分布特征。实验表明,改进方法在速度和命中准确率上都得到提高。更多还原

【Abstract】 With the rapid development of technologies of computer and multimedia and the wide-spread use of Internet, there is more and more of image information. How to rapidly find out images needed is starving for resolve. Content-based Image Retrieval (CBIR) is aimed at searching out images which satisfy user’s needs, so is widely researched. Fuzzy set theory advances development of CBIR, extends CBIR from precision of calculating and caters for human’s fuzzy thoughts. Emphasizing on this character, the thesis discusses image retrieval techniques which are syncretized Fuzzy set, analyzes and compares the characters of Fuzzy c-Means clustering and artificial immune network aiNet clustering, finally presents an improved region-based fuzzy feature to content-based image retrieval better in speed and hit rate than the original method as the realized system proves. The main works are as follows. ¨¨To summarize key techniques of modules of CBIR system such as feature extraction, indexing scheme, similarity measure, query specification, relevance feedback and performance evaluation. ¨¨To discuss some fuzzy techniques of feature extraction including crude fuzzy histograms, fuzzy paradigm-based histograms, combined fuzzy histograms and fuzzy geometrical features. ¨¨To analyze and compare the characters of Fuzzy c-Means clustering and artificial immune network aiNet clustering, conclude that Fuzzy c-Means clustering does not work well if the underlying classes or clusters deviate strongly from hyperspherical structures, aiNet is capable of reducing redundancy, describing data structure, including the shape of clusters by experiments on relatively gathering data set, scattering data set, annular data set and corkscrew data set. ¨¨To present and realize an improved region-based fuzzy feature to content-based image retrieval. On the basis of original method, three improvements are presented, they are data reduction but no influence to results by quantization and aggregation to Fuzzy c-Means, describing regions by shape based moments and adding feature of spatial information by neighboring table. Experiment results show that the new is better in speed and hit rate.更多还原