【作者】 李倩倩；

【导师】 阳爱民；

【作者基本信息】 湖南工业大学 ， 计算机应用技术， 2008， 硕士

【摘要】 如何有效地组织、管理和充分利用多媒体信息资源,如何快速、高效地查询、检索所需要的多媒体信息是当今媒体信息处理与理解领域的研究热点。对于非结构化的图像数据,传统的基于文本的检索方法和基于内容的检索方法不能解决基于高层语义的图像检索中存在的问题。在实际应用中,用户需要的是对图像即高层概念特征的查询。本文旨在弥合可感知的媒体视觉特征与高层概念特征间的“语义鸿沟”,设计并实现了一个基于语义的图像标注和检索原型系统,该研究的创新点体现在以下三个方面:1.构建特定领域的图像语义知识库和新的MPEG-7描述方案利用本体规范化描述领域知识的特点,在图像的对象和对象关系层上,建立一个针对特定领域的图像语义知识库。对MPEG-7的多媒体描述方案进行扩展,得到新的描述方案,提供了能与MPEG-7标准的描述相互转换的扩展描述机制。2.图像语义的获取和标注采用图像分割算法获取图像包含的对象信息,与用户进行交互,通过区域融合获取最终的图像对象区域。根据图像语义本体库描述出图像的内容,采用MPEG-7标准和图形化的表示方式对语义内容进行组织,获得对图像语义理解的最终表示形式。3.基于语义的图像检索基于图形化的图像语义标注模型,建立基于路径的索引机制,对图像语义标注文档进行检索。在检索过程的实现中,利用Lucene搜索引擎技术建立索引,利用Xpath对XML文档的操作,实现了基于语义的图像检索。在实验中,与全文检索方法进行比较。分别对这两种方法得到的查询结果计算查全率和查准率。实验表明本文提出的图像语义标注和检索系统可有效实现基于语义的图像检索。更多还原

【Abstract】 With the development of computer and network technology, the research focuses how to effectively organize, manage and fully use multimedia information, how to fast and effectively query and retrieval needful them in current domain of media information process and understanding. For unstructured image data, the traditional textual retrieval method and the Content-Based Image Retrieval (CBIR) can not solve the problems in semantic based image retrieval. In the practical application, users need to query images based on the high level concept features. In order to bridge the“semantic gap”between visual features and concept features, this paper designs and implements a prototype system of image annotation and retrieval based on semantic. Its novelty includes the following three aspects:1. Construct image semantic knowledge for special domain and new MPEG-7 description scheme.On the level of the objects and their relationships in the image, an image semantic knowledge base for special domain is constructed by adopting the feature of ontology specification to describe domain knowledge. A new description scheme is acquired by extending MPEG-7 multimedia description scheme, providing a new description mechanism that can be conversed to the description in MPEG-7.2. Acquire and annotate of image semanticIn the paper, image segment algorithm is adopted to acquire its objects, and regional integration technology is utilized to acquire the final image semantic objects with user’s interaction. According to the semantic ontology knowledge, MPEG-7 and graphical method are adopted to organize image semantic content, which is the final format of user’s understanding.3. Retrieve images based on semanticAccording to the graphical form of semantic, the indexing mechanism based on path is constructed to implement the image retrieval based on semantic. In the implementation of retrieval system, Lucene search engine is adopted to construct the index, and Xpath is utilized to process XML documents.The corresponding experimental data is compared with the method of full text on recall and precision. The results testify that our method can effectively implement semantic based image retrieval.更多还原