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Communication Dans Un Congrès Année : 2012

Finding Groups of Duplicate Images In Very Large Dataset

Résumé

This paper addresses the problem of detecting groups of duplicates in large-scale unstructured image datasets such as the Internet. Leveraging the recent progress in data mining, we propose an efficient approach based on the search of closed patterns. Moreover, we present a novel way to encode the bag-of-words image representation into data mining transactions. We validate our approach on a new dataset of one million Internet images obtained with random searches on Google image search. Using the proposed method, we find more than 80 thousands groups of duplicates among the one million images in less than three minutes while using only 150 Megabytes of memory. Unlike other existing approaches, our method can scale gracefully to larger datasets as it has linear time and space (memory) complexities. Furthermore, the approach does not need (to build or use) any precomputed indexing structure.
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Dates et versions

hal-00806196 , version 1 (29-03-2013)

Identifiants

  • HAL Id : hal-00806196 , version 1

Citer

Winn Voravuthikunchai, Bruno Crémilleux, Frédéric Jurie. Finding Groups of Duplicate Images In Very Large Dataset. Proceedings of the British Machine Vision Conference (BMVC 2012), Sep 2012, Guildford, United Kingdom. pp.105.1--105.12. ⟨hal-00806196⟩
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