7 February 2011 Document similarity measures and document browsing
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Abstract
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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Ildus Ahmadullin, Ildus Ahmadullin, Jian Fan, Jian Fan, Niranjan Damera-Venkata, Niranjan Damera-Venkata, Suk Hwan Lim, Suk Hwan Lim, Qian Lin, Qian Lin, Jerry Liu, Jerry Liu, Sam Liu, Sam Liu, Eamonn O'Brien-Strain, Eamonn O'Brien-Strain, Jan Allebach, Jan Allebach, } "Document similarity measures and document browsing", Proc. SPIE 7879, Imaging and Printing in a Web 2.0 World II, 787909 (7 February 2011); doi: 10.1117/12.877268; https://doi.org/10.1117/12.877268
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