Web mining for Web image retrieval
Web mining for Web image retrieval
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DOI：
10.1002/asi.1132.abs
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发表时间：
2001-08
期刊：
影响因子：
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通讯作者：
Zheng Chen;Wenyin Liu;Feng Zhang;Mingjing Li;HongJiang Zhang
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文献类型：
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作者：
Zheng Chen;Wenyin Liu;Feng Zhang;Mingjing Li;HongJiang Zhang
由于数字成像技术的进步和互联网的便利，数字图像的普及率正在迅速增加。然而，如何从互联网上找到用户预期的图像并不是一件容易的事。主要原因是Web图像通常不使用语义描述符进行标注。在这篇文章中，我们提出了一种有效的方法和一个原型系统的图像检索从互联网上使用Web挖掘。该系统还可以作为网络图像搜索引擎。该方法的关键思想之一是提取网页上的文本信息，对图像进行语义描述。然后在图像相似度评估中将文本描述与其他低层图像特征相结合。本文的另一个主要贡献是将数据挖掘应用到用户反馈日志中，从三个方面提高了图像检索的性能。首先，通过去除杂乱和不相关的文本信息来提高从Web页面获得的图像表示的文档空间模型的准确性。其次，构建用户图像表示的用户空间模型，并将其与文档空间模型相结合，以消除页面作者的表达与用户的理解和期望之间的不匹配。第三，发现低层特征和高层特征之间的关系，这对于相似度评估中低层特征的权重分配是非常有用的。
The popularity of digital images is rapidly increasing due to improving digital imaging technologies and convenient availability facilitated by the Internet. However, how to find user-intended images from the Internet is nontrivial. The main reason is that the Web images are usually not annotated using semantic descriptors. In this article, we present an effective approach to and a prototype system for image retrieval from the Internet using Web mining. The system can also serve as a Web image search engine. One of the key ideas in the approach is to extract the text information on the Web pages to semantically describe the images. The text description is then combined with other low-level image features in the image similarity assessment. Another main contribution of this work is that we apply data mining on the log of users' feedback to improve image retrieval performance in three aspects. First, the accuracy of the document space model of image representation obtained from the Web pages is improved by removing clutter and irrelevant text information. Second, to construct the user space model of users' representation of images, which is then combined with the document space model to eliminate mismatch between the page author's expression and the user's understanding and expectation. Third, to discover the relationship between low-level and high-level features, which is extremely useful for assigning the low-level features' weights in similarity assessment.