Web mining for Web image retrieval

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 non-trivial. The main reason is that the web images are usually not annotated using semantic descriptors. In this paper, 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 user’s 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.