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Small objects query suggestion in a large web-image collection

Pierre Letessier 1, 2 Nicolas Hervé 1 Julien Champ 2 Alexis Joly 2 Olivier Buisson 1 Amel Hamzaoui 2 
2 ZENITH - Scientific Data Management
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : State-of-the-art visual search methods allow retrieving efficiently small rigid objects in very large image datasets (e.g. logos, paintings, etc.). User's perception of the classical query-by-window paradigm is however affected by the fact that many submitted queries actually return nothing or only junk results. We demonstrate in this demo that the perception can be radically different if the objects of interest are rather suggested to the user by pre-computing relevant clusters of instances. Impressive results involving very small objects discovered in a web collection of 110K images are demonstrated through a simple interactive GUI.
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Submitted on : Monday, November 25, 2013 - 2:40:20 PM
Last modification on : Tuesday, September 6, 2022 - 4:53:26 PM




Pierre Letessier, Nicolas Hervé, Julien Champ, Alexis Joly, Olivier Buisson, et al.. Small objects query suggestion in a large web-image collection. ACM Multimedia, Oct 2013, Barcelone, Spain. ⟨10.1145/2502081.2502248⟩. ⟨hal-00908891⟩



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