Approximate nearest neighbors using sparse representations

Joaquin Zepeda 1 Ewa Kijak 2, * Christine Guillemot 1
* Corresponding author
1 TEMICS - Digital image processing, modeling and communication
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
2 TEXMEX - Multimedia content-based indexing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : A new method is introduced that makes use of sparse image representations to search for approximate nearest neighbors (ANN) under the normalized inner-product distance. The approach relies on the construction of a new sparse vector designed to approximate the normalized inner-product between underlying signal vectors. The resulting ANN search algorithm shows significant improvement compared to querying with the original sparse vectors. The system makes use of a proposed transform that succeeds in uniformly distributing the input dataset on the unit sphere while preserving relative angular distances.
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https://hal.inria.fr/inria-00561778
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Submitted on : Tuesday, February 1, 2011 - 6:25:08 PM
Last modification on : Friday, November 16, 2018 - 1:22:17 AM

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Joaquin Zepeda, Ewa Kijak, Christine Guillemot. Approximate nearest neighbors using sparse representations. IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP'10, IEEE, Mar 2010, Dallas, TX, United States. ⟨10.1109/ICASSP.2010.5496145⟩. ⟨inria-00561778⟩

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