【作者】 张华；

【导师】 孟祥增；

【作者基本信息】 山东师范大学 ， 教育技术学， 2004， 硕士

【摘要】 针对日益增长的网络图像，本文研究了基于HTML文档的WWW图像语义信息提取方法，在语义层次上实现了对图像的自动标注和分析。本项研究对基于内容和语义的WWW图像检索具有重要意义。 HTML文档作为WWW图像的外部信息源和载体，蕴涵了丰富的描述图像内容的文本信息。HTML文档中的图像名、图像注释、图像周围文本、图像URL、图像所在网页URL及标题、图像超链接网页URL及标题等在一定程度上反映了图像的关键内容和语义。为了从这些与图像相关的文本中提取图像的语义信息，本文根据图像的视觉属性和语义属性提出了一种图像语义表征模型，并建立了相应的语义词典，包括图像主题词分类词典、图像主体词分类词典、图像主体属性词典和用来翻译汉语拼音、英文单词或缩写词的图像主题词对照词典。 根据图像的相关文本和语义词典提取图像语义信息的基本过程分为三步：(1)利用图像主题词翻译词典，将图像相关文本中出现的汉语拼音、英文单词或缩写词翻译成中文主题词；(2)将图像相关文本自动分词和词性标注；(3)利用图像语义词典，采用基于规则与基于统计相结合的方法，从图像相关文本的分词标注字符串中提取反映图像语义信息的主题词、主体词及其属性词。 基于上述思想，本文研发了一个WWW图像语义提取系统。该系统由图像元搜索和预处理、图像语义信息提取及主题词在线学习三个模块构成。图像元搜索和预处理模块负责从Google与Baidu上搜索图像与相关网页，并提取图像相关文本。图像语义信息提取模块负责从图像相关文本中提取图像主题词、主体词及其属性词。主题词在线学习模块负责主题词分类词典的在线学习与自动添加。 本文最后给出了提取WWW图像语义信息的实验结果。实验表明，提取图像主题词、主体词及其属性词的平均覆盖率为52％，平均准确率为44％。实验说明，这种图像语义信息提取方法，在基于内容和语义的WWW图像检索中具有较高的应用价值。更多还原

【Abstract】 As to images being heavily increased in WWW, A method of extracting image semantics based on HTML documents was given in this paper which could automatically index and analyze images on semantic level. This method is very significant to content-based and semantic-based image retrieval in WWW.As external information sources and carriers of images in WWW, HTML documents contained plentiful text informations which related closely to image content and image semantics such as image name, image annotation, image surrounding texts, image URL, web page title and URL of image, hyperlink web page title and URL of image etc..In order to extract image semantics in WWW from the text informations said above, a semantic representation model of image was built by its semantic and visual attributes. At the same time, four image semantic dictionaries also were built based on the semantic representation model of image. These semantic dictionaries include image classification dictionary of topic words, image classification dictionary of mainbody words, image classificatioa dictionary of mainbody-attribute words .and image antitheses dictionary of topic words which was used to translate the Chinese Pin Yin,English words and abbreviative words into Chinese topic words.Based on the semantic dictionaryies and texts related to image in WWW, the process of extracting image semantics is made up of three steps. The first,use image antitheses dictionary of topic words to translate the Chinese Pin Yin, English wordd and abbreviative wordd into Chinese topic words. The second, segment the texts related to image in WWW into words and annotate their part of speech.The last,use regulation-based andstatistic-based approach to extract topic words of image,mainbody words of image and mainbody-attribute words form the segmentation and annotation result of the texts related to image in WWW.From the ideas said above, a semantic extracting system of image in WWW was developed in this paper. This system includes three parts. They are meta-search and pretreatment module, semantics extracting module and topic words online learning module. Meta-search and pretreatment module is responsible for searching images from Google or baidu search engine and extracting related texts from web pages related to image in WWW. Semantics extracting module is responsible for extracting topic words, mainbody words and mainbody-attribute words from related texts of image in WWW. Topic words online learning module is responsible for finding new topic words from related texts of image in WWW and adding them automatically to the image classification dictionary of topic words.At last, an experimental result of extracting image semantics in WWW was given in this paper. The average overlay rate was 52% and the average nicety rate was 44%.The experimental result showed that the extracting method of image semantics in WWW has highly applied value to content-based and semantic-based retrieval of image in WWW.更多还原