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...

• 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)
• Keywords: Computer Vision, CVPR 2009, Image Retrieval, Local Image Descriptors, Web Image Search