Scalable Recognition with a Vocabulary Tree

A recognition scheme that scales efficiently to a large number of objects is presented. The efficiency and quality is exhibited in a live demonstration that recognizes CD-covers from a database of 40000 images of popular music CD's. The scheme builds upon popular techniques of indexing descript...

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Bibliographic Details
Published in2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06) Vol. 2; pp. 2161 - 2168
Main Authors Nister, D., Stewenius, H.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2006
Subjects
Computer vision
Frequency
Image databases
Image recognition
Indexing
Quantization
Robustness
Spatial databases
Visualization
Vocabulary
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Summary:A recognition scheme that scales efficiently to a large number of objects is presented. The efficiency and quality is exhibited in a live demonstration that recognizes CD-covers from a database of 40000 images of popular music CD's. The scheme builds upon popular techniques of indexing descriptors extracted from local regions, and is robust to background clutter and occlusion. The local region descriptors are hierarchically quantized in a vocabulary tree. The vocabulary tree allows a larger and more discriminatory vocabulary to be used efficiently, which we show experimentally leads to a dramatic improvement in retrieval quality. The most significant property of the scheme is that the tree directly defines the quantization. The quantization and the indexing are therefore fully integrated, essentially being one and the same. The recognition quality is evaluated through retrieval on a database with ground truth, showing the power of the vocabulary tree approach, going as high as 1 million images.
ISBN:9780769525976
0769525970
ISSN:1063-6919
DOI:10.1109/CVPR.2006.264