权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Image Retrieval Based on Fuzzy Set Theory and Development of an Experimental System.

基于模糊集理论的图像检索和实验系统的开发。

基本信息

批准号：
01550321
负责人：
MIYAKE Teruhisa
金额：
$ 1.41万
依托单位：
University of Tsukuba
依托单位国家：
日本
项目类别：
Grant-in-Aid for General Scientific Research (C)
财政年份：
1989
资助国家：
日本
起止时间：
1989 至 1990
项目状态：
已结题

项目摘要

Recently, as the result of the development of the researches on multimedia databases and hypertexts, image retrieval is rapidly becoming more important technique in those fields. In the past, some methods have been used for the image retrieval. For example, the similar image retrieval method, in which a given picture is used for a query and the retrieved result is the most similar picture in a image database, and the method of using network index are well known. On image indexing and retrieving, the pattern recognition techniques and the products of cognitive science are basic and useful techniques generally. On the other hand, image data has some sort of uncertainty or fuzziness in itself and it is natural to apply fuzzy set theory to image retrieval, but the method of fuzzy image retrieval is not proposed yet.In this research, we proposed new method of image retrieval based on fuzzy set theory and implement an experimental image retrieval system. The summary of the research is shown below :1. The propositional network model was proposed for the image memory process of human beings. Based on this model, the set of fuzzy propositions are used as the image index.2. We assumed that the query is composed a set of fuzzy propositions that has the same structure of image database indexes. Using this type of query, we can make various user interfaces in which user query can be a set of keywords or an expression in natural language or a reference image.3. We proposed a fuzzy model for measuring a matching degree between a query and a index and produce an algorithm which calculates a matching degree efficiently. This degree is used as a fuzzy membership value on the retrieving process of the image database.4. We implemented an experimental image retrieval system using this method.

近年来，随着对多媒体数据库和超文本研究的不断深入，图像检索正迅速成为这些领域中的重要技术。在过去，一些方法已经被用于图像检索。例如，相似图像检索方法和使用网络索引的方法是公知的，在相似图像检索方法中，给定图片用于查询并且检索结果是图像数据库中最相似的图片。在图像检索中，模式识别技术和认知科学的成果是最基本、最有用的技术。另一方面，图像数据本身具有一定的不确定性或模糊性，将模糊集理论应用于图像检索是很自然的，但模糊图像检索的方法还没有被提出，本文提出了一种基于模糊集理论的图像检索方法，并实现了一个实验性的图像检索系统。本研究的主要内容如下：1.针对人类的图像记忆过程，提出了命题网络模型。基于该模型，以模糊命题集作为图像索引.我们假设查询是由一组模糊命题组成的，这些模糊命题具有与图像数据库索引相同的结构。使用这种类型的查询，我们可以制作各种用户界面，其中用户查询可以是一组关键字或自然语言的表达式或参考图像.本文提出了一种度量查询与索引匹配度的模糊模型，并给出了一种有效的匹配度计算算法。在图像数据库的检索过程中，将该隶属度作为模糊隶属度值.我们使用这种方法实现了一个实验性的图像检索系统。