Content based indexing of multimedia has always been a challenging task. The enormity and the diversity of the multimedia content on the web adds another dimension to this challenge. In this paper, we examine ways of combining visual and textual information for content based indexing of multimedia on the web. In particular, we examine different methods of combining evidences due to face detection, Text/HTML analysis and face recognition for identifying person images. We provide experimental evaluation of the following strategies: i) Face detection on the image followed by Text/HTML analysis of the containing page; ii) face detection followed by face recognition; iii) face detection followed by a linear combination of evidences due to text/HTML analysis and face recognition; and iv) face detection followed by a Dempster-Shafer combination of evidences due to text/HTML analysis and face recognition. These strategies were implemented in an automatic web search agent named Diogenes1 andcompared against some well known web image search engines. The latter includes commercial systems such as Alta Vista, Lycos and Ditto, and a research prototype, WebSEEk. We report the results of our experimental retrievals where Diogenes outperformed these search engines for celebrity image queries in terms of average precision.
基于内容的多媒体索引一直是一项艰巨的任务。 Web上多媒体内容的庞大和多样性为这一挑战增加了另一个维度。在本文中,我们研究了将视觉和文本信息相结合的方法,以用于基于内容的Web多媒体索引。特别是,我们研究了由于面部检测,文本/ HTML分析和面部识别而用于识别人像的证据组合的不同方法。我们提供以下策略的实验评估:i)在图像上进行人脸检测,然后对包含页面进行文本/ HTML分析; ii)人脸检测后再进行人脸识别; iii)人脸检测,然后由于文本/ HTML分析和人脸识别而线性组合证据; iv)由于文本/ HTML分析和面部识别,面部检测之后是证据的Dempster-Shafer组合。这些策略是在名为Diogenes 1 SUP>的自动Web搜索代理中实现的,并且与某些著名的Web图像搜索引擎进行了比较。后者包括Alta Vista,Lycos和Ditto等商业系统,以及研究原型WebSEEk。我们报告了实验性检索的结果,其中Diogenes的名人像查询在平均准确度方面优于这些搜索引擎。 P>
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