【作者】 王栋；

【导师】 景晓军；

【作者基本信息】 北京邮电大学 ， 电子与通信工程（专业学位）， 2013， 硕士

【摘要】 随着多媒体技术的发展和数字化应用的不断推广,基于内容的图像检索系统日益成为多媒体检索领域的研究热点。它具有良好的市场前景和研究潜力,通过对这些关键技术的研究,可以有效的提高图像检索的效率。本文首先介绍了基于内容的图像检索研究的背景和意义,阐述了国内外发展现状进行分析,论述并分析了基于内容的图像检索相关关键技术,包括图像特征描述和图像局部特征。在图像特征描述中,阐述了图像的颜色、纹理和形状等低层特征。在图像局部特征中,阐述了SIFT算法,该算法以其出色的描述能力和鲁棒性,得到了大量应用。针对图像低层特征和高层语义之间的语义鸿沟问题,本文采用了“视觉词”的概念,通过对提取的SIFT特征进行聚类,得到图像视觉词,进而生成视觉词典。在聚类过程中,本文采用了聚类效果更好的FCM聚类算法,而不是常用的K均值聚类算法,不过由于计算复杂度更高,因此牺牲了一定的计算速度。同时,考虑到在聚类过程中丢失了图像空间信息,本文采用图像金字塔的方式加入空间信息,最后得到了图像特征向量：空间视觉词分布密度直方图。在进行图像的相似性度量时,本文采用了直方图相交法。最后,本文将所采用的“SIFT+FCM+金字塔”方法在图像库中进行了测试,证明了本算法的有效性,并对仿真结果进行了分析,指明了下一步需要改进之处以及今后的研究方向。更多还原

【Abstract】 With the development of multimedia technology and the digital application, content-based image retrieval system has become the focus of multimedia retrieval research. It has bright market prospective and research potential, the efficiency of image retrieval will be improved significantly through the research of those key technologies.First, the background and meaning of content-based image retrieval is introduced in this thesis,which states the current image retrieval systems. Then it analysis the development status at home and abroad,through the analysis of those retrieval systems, it discusses the relative key technologies of content-based image retrieval.In view of the theoretical principle, this thesis depicts several key technologies involved in image retrieval in detail including the image feature description, such as color, texture and shape feature, and the image local features. In image local features, SIFT algorithm possesses the remarkable descriptive power and robustness, and gains generous applications. In order to overcome the semantic gap between low level feature and high level semantic, this thesis introduces the concept "visual words", after abstracting the SIFT feature, and it uses the clustering algorithm to generate image visual words and processes to the next step—“visual dictionary”. In the clustering procedure, this thesis uses the FCM algorithm and the common used K-means clustering algorithm, which gains more accurate results. As the FCM algorithm has more computation complexity, it’s calculation speed become lower. Meanwhile, in the clustering process, some image space information lost,and this thesis adopts the image pyramids algorithms to add space information, and finally get the image feature vectorspace visual word distribution density histogram. In the similarity measurement stage, this thesis uses the Histogram intersection method.In the last part, this paper tests the “SIFT+FCM+PYRAMID” algorithms on the image library, and proves effectiveness, then it analysis the simulation result and point out the future research direction.更多还原