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Améliorer la recherche par similarité dans une grande base d'images fixes par des techniques de fouilles de données

Anicet Kouomou-Choupo 1
1 TEXMEX - Multimedia content-based indexing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : Still images can be retrieved by similarity searching on global visual features such as color, texture or shape at the pixel level. Content-based retrieval systems then use and combine all the available low-level features whose computing cost can be prohibitive and they rank the images according to how well they match the submitted quaery-by-example. Finally, they return the best few matches to the used in a ranked result list. But, a subset of features could be sufficient enough to answer very quickly while offering an accepatble quality of results. Moreover, the administration of large collections of images accentuates the classical problems of indexing and efficiently querying inforamtion. Our work focuses on the elaboration of fully automatic and generic strategies of visual global features usage which exploit association rules to speed up the content-based retrieval on large still image databases. In this thesis, we also present how query-by-example proessing is adapted to propose intermadiate results that are progressively merged together with the advantage for the users, on one hand, not to wait until the whole database has been processed and, on the other hand, to allow them to stop the current execution of the query without losing the first partial results. Experiments performed on real image sets show that our method improves retrieval times. They also confirm the interest of global features combination for content-based image retrieval.
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Contributor : Patrick Gros <>
Submitted on : Thursday, October 7, 2010 - 7:37:23 PM
Last modification on : Thursday, January 7, 2021 - 4:13:51 PM
Long-term archiving on: : Monday, January 10, 2011 - 11:35:07 AM


  • HAL Id : tel-00524418, version 1


Anicet Kouomou-Choupo. Améliorer la recherche par similarité dans une grande base d'images fixes par des techniques de fouilles de données. Interface homme-machine [cs.HC]. Université Rennes 1, 2006. Français. ⟨tel-00524418⟩



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