Efficient Dimension Reduction Of Global Signature With Sparse Projectors For Image Near Duplicate Retrieval

Romain Negrel 1 David Picard 1 Philippe-Henri Gosselin 2, 1
1 MIDI
ETIS - Equipes Traitement de l'Information et Systèmes
2 TEXMEX - Multimedia content-based indexing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : In this paper, we tackle the storage and computational cost of linear projections used in dimensionality reduction for near duplicate image retrieval. We propose a new method based on metric learning with a lower training cost than existing methods. Moreover, by adding a sparsity constraint, we obtain a projection matrix with a low storage and projection cost. We carry out experiments on a well known near duplicate image dataset and show our algorithm behaves correctly. Retrieval performances are shown to be promising when compared to the memory footprint and the projection cost of the obtained sparse matrix.
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Submitted on : Monday, September 15, 2014 - 1:48:13 PM
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Romain Negrel, David Picard, Philippe-Henri Gosselin. Efficient Dimension Reduction Of Global Signature With Sparse Projectors For Image Near Duplicate Retrieval. IAPR International Conference on Pattern Recognition, Aug 2014, Stockholm, Sweden. 6 p. ⟨hal-01064050⟩

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