Cross-indexing of binary SIFT codes for large-scale image search

In recent years, there has been growing interest in mapping visual features into compact binary codes for applications on large-scale image collections. Encoding high-dimensional data as compact binary codes reduces the memory cost for storage. Besides, it benefits the computational efficiency since...

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Publié dans:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 23(2014), 5 vom: 07. Mai, Seite 2047-57
Auteur principal: Liu, Zhen (Auteur)
Autres auteurs: Li, Houqiang, Zhang, Liyan, Zhou, Wengang, Tian, Qi
Format: Article en ligne
Langue:English
Publié: 2014
Accès à la collection:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Sujets:Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.
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520 |a In recent years, there has been growing interest in mapping visual features into compact binary codes for applications on large-scale image collections. Encoding high-dimensional data as compact binary codes reduces the memory cost for storage. Besides, it benefits the computational efficiency since the computation of similarity can be efficiently measured by Hamming distance. In this paper, we propose a novel flexible scale invariant feature transform (SIFT) binarization (FSB) algorithm for large-scale image search. The FSB algorithm explores the magnitude patterns of SIFT descriptor. It is unsupervised and the generated binary codes are demonstrated to be dispreserving. Besides, we propose a new searching strategy to find target features based on the cross-indexing in the binary SIFT space and original SIFT space. We evaluate our approach on two publicly released data sets. The experiments on large-scale partial duplicate image retrieval system demonstrate the effectiveness and efficiency of the proposed algorithm 
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700 1 |a Tian, Qi  |e verfasserin  |4 aut 
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