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