ImageSeer: Clustering and Searching WWW Images Using Link and Page Layout Analysis

Due to the rapid growth of the number of digital images on the Web, there is an increasing demand for effective and efficient method for organizing and retrieving the images available. This paper describes ImageSeer, a system for clustering and searching WWW images. By using a vision-based page segmentation algorithm, a web page is partitioned into blocks, and the textual and link information of an image can be accurately extracted within the block containing that image. The textual information is used for image representation. By extracting the page-to-block, block-to-image, block-to-page relationships through link structure and page layout analysis, we construct an image graph. Our method is less sensitive to noisy links than previous methods like PicASHOW, and hence the image graph can better reflect the semantic relationship between images. With the graph models, we use techniques from spectral graph theory and Markov Chain theory for image ranking, clustering and embedding. Some experimental results are given in the paper.