Object recognition in extended image databases using a mobile client-server architecture

Abstract : This paper presents a novel approach for object recognition in extended image databases using a mobile client server architecture. The proposed approach relies upon feature detection and description to characterize textured objects within the image. The similarity search is performed on descriptor arrays by computing the distance between the query descriptor compared with reference descriptors extracted offline. The key contributions of the approach are the high accuracy, the time-effectiveness and the scalability of the method towards large image datasets. The developed method is first, integrated on a mobile platform and, then, deployed on a client server architecture to deal with high volume image galleries. Experiments are performed to evaluate the performances of the system in real-life environment conditions and the obtained results demonstrate the relevance of the proposed approach
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Conference papers
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https://hal.archives-ouvertes.fr/hal-01298093
Contributor : Médiathèque Télécom Sudparis & Institut Mines-Télécom Business School <>
Submitted on : Tuesday, April 5, 2016 - 2:45:19 PM
Last modification on : Friday, January 24, 2020 - 1:23:22 AM

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Yassine Lehiani, Marius Preda, Madjid Maidi, Mircea Adrian Gabrielli. Object recognition in extended image databases using a mobile client-server architecture. ICSIPA 2015 : International Conference on Signal and Image Processing Application, Oct 2015, Kuala Lumpur, Malaysia. pp.197 - 202, ⟨10.1109/ICSIPA.2015.7412189⟩. ⟨hal-01298093⟩

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