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 algo-rithm, 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 Pi-cASHOW, and hence the image graph can better reflect the se-mantic 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 ex-perimental results are given in the paper.

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