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 of more than one million images, show that our

scheme achieves a 49% improvement in average precision

over the baseline bag-of-words approach. Retrieval performance

is comparable to existing full geometric verification

approaches while being much less computationally expensive.

When combined with full geometric verification

we achieve a 77% precision improvement over the baseline

bag-of-words approach, and a 24% improvement over full

geometric verification alone.

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In: CVPR 2009

Publisher: IEEE
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