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• W2529838999 abstract "In this paper a supervised codebook learning technique for the Bag-of-Features representation that optimizes the learned codebooks towards face retrieval is proposed. This allows to use significantly smaller codebooks reducing both the storage requirements and the retrieval time allowing the proposed technique to efficiently scale to large datasets. The proposed method is also combined with a spatial image segmentation technique that exploits the natural symmetry of the human face to further reduce the size of the extracted representation. It is experimentally demonstrated using one large-scale face recognition dataset, the YouTube Faces Database, as well as two smaller datasets, that the proposed technique can increase the retrieval precision, while reducing the encoding time by almost two orders of magnitude." @default.
• W2529838999 created "2016-10-14" @default.
• W2529838999 creator A5061050264 @default.
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• W2529838999 date "2016-10-08" @default.
• W2529838999 modified "2023-09-27" @default.
• W2529838999 title "Spatial Bag of Features Learning for Large Scale Face Image Retrieval" @default.
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• W2529838999 doi "https://doi.org/10.1007/978-3-319-47898-2_2" @default.
• W2529838999 hasPublicationYear "2016" @default.
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