Bundling Features for Large Scale Partial-Duplicate Web Image Search

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Bundling Features for Large Scale Partial-Duplicate Web Image Search
In state-of-the-art image retrieval systems, an image is represented by a bag of visual words obtained by quantizing high-dimensional local image descriptors, and scalable schemes inspired by text retrieval are then applied for large scale image indexing and retrieval. Bag-of-words representations, however: 1) reduce the discriminative power of image features due to feature quantization; and 2) ignore geometric relationships among visual words. Exploiting such geometric constraints, by estimating a 2D affine transformation between a query image and each candidate image, has been shown to greatly improve retrieval precision but at high computational cost. In this paper we present a novel scheme where image features are bundled into local groups. Each group of bundled features becomes much more discriminative than a single feature, and within each group simple and robust geometric constraints can be efficiently enforced. Experiments in web image search, with a database o...
Zhong Wu (Tsinghua University), Qifa Ke (Microsoft
Added 05 May 2009
Updated 10 Dec 2009
Type Conference
Year 2009
Where CVPR
Authors Zhong Wu (Tsinghua University), Qifa Ke (Microsoft Research-ISRC), Michael Isard (Microsoft Research), Jian Sun (Microsoft Research Asia)
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