Document visual similarity measure for document search
| Autor: | Eamonn O'Brien-Strain, Niranjan Damera-Venkata, Seungyon Lee, Jerry J. Liu, Jian Fan, Ildus Ahmadullin, Jan P. Allebach, Qian Lin |
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| Rok vydání: | 2011 |
| Předmět: |
Information retrieval
Computer science business.industry Nearest neighbor search Pattern recognition Document clustering Similarity measure Visual appearance Similarity (network science) ComputingMethodologies_DOCUMENTANDTEXTPROCESSING Artificial intelligence Document retrieval Hellinger distance business Document layout analysis |
| Zdroj: | ACM Symposium on Document Engineering |
| DOI: | 10.1145/2034691.2034722 |
| Popis: | Managing large document databases has become an important task. Being able to automatically compare document layouts and classify and search documents with respect to their visual appearance proves to be desirable in many applications. We propose a new algorithm that approximates a metric function between documents based on their visual similarity. The comparison is based only on the visual appearance of the document without taking into consideration its text content. We measure the similarity of single page documents 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 the components of different documents are calculated as an approximation of the Hellinger distance between corresponding distributions. Since the Hellinger distance obeys the triangle inequality, it proves to be favorable in the task of nearest neighbor search in a document database. Thus, the computation required to find similar documents in a document database can be significantly reduced. |
| Databáze: | OpenAIRE |
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