【作者】 张明；

【导师】 沈祖诒； 王志坚；

【作者基本信息】 河海大学 ， 水利水电工程， 2003， 博士

【摘要】 随着图象数据的大量涌现，基于内容的图象检索技术已成为研究热点，其目的是为了对大容量图象库提供有效的检索手段。本文将信息论和模糊集理论运用于基于内容图象检索方法的研究，重点研究了基于信息熵的图象检索技术、图象相似性度量方法、模糊检索和动态相关反馈机制。以防汛决策信息查询为背景，探讨了基于内容工情图象检索系统的设计与实现中的关键技术。本文的主要研究工作如下：1．从人类色彩感知的角度出发，研究了用于图象检索的色彩空间，特征提取和相似性度量等问题，将HSL色彩空间进行了划分，对每两种色彩之间的相似度进行了重新定义，便其更好地符合人对图象色彩的辨别能力。并将色彩直方图进行了扩充，解决了两幅图象所含色彩数目不同时如何进行匹配的问题。2．将信息论中的信息熵概念引入图象检索，定义了图象墒空间、熵差的概念，研究了图象熵的性质，探讨了基于图象熵的相似性度量方法和实现算法，该方法可将n维直方图空间变为1维，提高了图象检索的效率，改善了图象检索系统的性能。3．提出了基于信息熵的多精度相似匹配和多维散列索引技术。多精度色彩匹配将图象划分成了许多子图块，每一子图都具有其相应的色彩直方图与图象墒，作为细粒度匹配的基础。同时证明了在熵空间采用多精度分级检索的正确性，并能更好地支持子图象的查询。4．研究了模糊检索方法和相关反馈机制在图象检索中的应用，提出了一种模糊图象数据模型和模糊空间的概念，该模型将可视特征、空间特征、语义特征看作超属性，既充分利用了传统关系数据库的优点，同时又考虑了图象数据以及模糊查询的特点，文中提出的模糊空间和模糊相似性度量方法能支持基于模糊特征的图象查询，较好地体现用户图象查询的应用需求，文中定义的语义模板和相关反馈机制能在一定程度上表达用户的查询语义，提高图象检索的准确率和易用性。5．研究了图象检索技术在水利行业中的应用，提出了关系—层次—面向对象的混合数据模型，并扩展数据模型以满足多媒体数据与空间数据的特殊情况，摘要研究了系统实现的具体技术方案，设计并实现了一个用于工情图象检索模型系统。更多还原

【Abstract】 As very large collections of images are becoming common, there is a growing interest in image database that can be quired based on image content. Content-Based Image Retrieval (CBIR) has become an important research issue aiming at providing effective means for image retrieval on large image databases. In the dissertation, content-based image retrieval methods are studied based on information theory and fuzzy set theory, and several techniques, information entropy-based image retrieval, similarity measurement of image entropy, fuzzy index, dynamic relevant feedback are presented. In the end, the research results are applied to design and implement the content-based engineering image retrieval system, and some key techniques are presented for inquiring about flood prevention image information.The main contributions of this research include:1. According to principles and human perception of color, a HSL (Hue Saturation Light) color space, which is perceptively consistent with human vision, is selected and divided. The similarity between two colors is re-defined in order to accord with human discernment for colors. The method of extending color histogram is presented to match images with different colors.2. Defining the entropy space of image and entropy difference, the concept of information entropy is applied to image retrieval. Some mathematical properties of entropy are studied, and similarity measurement of image entropy and corresponding algorithm is presented.These techniques can reduce the dimensionality of histogram space from n to l(n>l), increase the image retrieval efficiency, and improve the capability of image retrieval system.3. Based information entropy, multi-precision similarity matching and multi-dimension hash index are proposed. The image is divided into a number of sub blocks, each with its associated color histogram and image entropy. Theoretics and experimental results show that the techniques can help to make similarity matching more precise.4. A fuzzy image data model and a concept of fuzzy space are proposed, in which model visual feature, spatial feature and semantic feature are used for super feature in order to utilize advantage of traditional relation database as well as characteristics of image data and fuzzy retrieval. Based fuzzy space, a method of similarity measurement of image is presented to support fuzzy features-based image retrieval and satisfy user’s query requirement for image. In the thesis, a semantic template and the mechanism of dynamic relevant feedback are defined so that it can express user’s query semantic and improve retrieval precision and useable capability for image retrieval.5. A mixed data model called relation-level-object, which is used to image retrieval system in flood prevention, is presented in order to satisfy special access requirement for image data and spatial data. With the research results of this dissertation, a prototype system, a content-based engineering image retrieval system, is designed and implemented.更多还原