Document similarity measures and document browsing
2011
Managing large document databases is an important task today. Being able to automatically com-
pare document layouts and classify and search documents with respect to their visual appearance
proves to be desirable in many applications. We measure single page documents' similarity with
respect to distance functions between three document components: background, text, and saliency.
Each document component is represented as a Gaussian mixture distribution; and distances between
dierent documents' components are calculated as probabilistic similarities between corresponding
distributions. The similarity measure between documents is represented as a weighted sum of the
components' distances. Using this document similarity measure, we propose a browsing mechanism
operating on a document dataset. For these purposes, we use a hierarchical browsing environment
which we call the document similarity pyramid. It allows the user to browse a large document dataset
and to search for documents in the dataset that are similar to the query. The user can browse the
dataset on dierent levels of the pyramid, and zoom into the documents that are of interest.
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