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- W2028503721 abstract "In this paper we propose a high-dimensional indexing technique, based on sparse approximation techniques to speed up the search and retrieval of similar images given a query image feature vector. Feature vectors are stored on an inverted indexed based on a sparsifying dictionary for l0 regression, optimized to reduce the data dimensionality. It concentrates the energy of the original vector on a few coefficients of a higher dimensional representation. The index explores the coefficient locality of the sparse representations, to guide the search through the inverted index. Evaluation on three large-scale datasets showed that our method compares favorably to the state-of-the-art. On a 1 million dataset of SIFT vectors, our method achieved 60.8% precision at 50 by inspecting only 5% of the full dataset, and by using only 1/4 of the time a linear search takes." @default.
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- W2028503721 date "2015-06-22" @default.
- W2028503721 modified "2023-10-18" @default.
- W2028503721 title "High-Dimensional Indexing by Sparse Approximation" @default.
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- W2028503721 doi "https://doi.org/10.1145/2671188.2749371" @default.
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