Abstract
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.
Keywords
VocabularyComputer scienceSearch engine indexingArtificial intelligenceScalabilityQuantization (signal processing)Tree (set theory)Scale-invariant feature transformClutterScheme (mathematics)Pattern recognition (psychology)Vector quantizationNatural language processingSpeech recognitionInformation retrievalComputer visionFeature extractionDatabaseMathematics
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Publication Info
- Year
- 2006
- Type
- article
- Volume
- 2
- Pages
- 2161-2168
- Citations
- 3595
- Access
- Closed
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Cite This
D. Nistér,
Henrik Stewénius
(2006).
Scalable Recognition with a Vocabulary Tree.
, 2
, 2161-2168.
https://doi.org/10.1109/cvpr.2006.264
Identifiers
- DOI
- 10.1109/cvpr.2006.264