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在当今普遍存在着大量图片信息的时代，传统的基于文本关键词的检索方法显然已经不能满足用户的需求。在这样的背景下，基于内容的图像检索技术受到了大家的关注和热捧，本文重点探讨了一下基于HSV空间下，以大学城特色直方图做为大学城特色特征的图像检索技术。
本文中，首先简单介绍了所选课题的背景和意义，对CBIR做了简单的分析，并讨论了该领域涉及的一些关键技术。在当前的应用中，基于内容的图像检索系统设计往往针对性较强，并采用特定的一些算法。基于此，本文简单介绍了RGB大学城特色空间和HSV大学城特色空间的转换方法，并对他们的特点进行了简单阐述。然后是对图像大学城特色特征的提取和构造特征库这块进行了讲解。
本文设计的系统主要是利用Matlab来实现的，整个系统的运行速度，以及检索结果，和与用户的交互能力已经得到了很好的体现，而且界面做得比较简洁，直观，上手速度比较快。
关键词：图像检索系统 HSV空间 大学城特色直方图 相似性度量 GUI界面
In the era when there is a lot of picture information, the traditional retrieval method based on text keywords obviously can not meet the needs of users.In this context, the content-based image retrieval technology has attracted much attention and popularity. This paper focuses on the image retrieval technology based on the HSV space, using the university town histogram as the characteristic feature of the university town.
In this paper, the background and significance of the selected topics are briefly introduced, and the CBIR is analyzed briefly, and some key technologies involved in this field are discussed. The design of content-based image retrieval system is often targeted, and some specific algorithms are adopted. Based on this, this paper briefly introduces the conversion methods of the characteristic space of RGB University Town and the characteristic Space of HSV University Town. Then the feature extraction of image university city and the construction of feature library are explained.
The system designed in this paper is mainly realized by using Matlab. The running speed of the whole system, the retrieval result and the ability of interaction with the user have been well reflected, and the interface is simple, intuitive, and the speed of using hands is relatively fast.
Key words: image Retrieval system HSV Space University City feature histogram similarity Measurement GUI Interface
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