Object retrieval with large vocabularies and fast spatial matching

In this paper, we present a large-scale object retrieval system. The user supplies a query object by selecting a region of a query image, and the system returns a ranked list of images that contain the same object, retrieved from a large corpus. We demonstrate the scalability and performance of our...

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Vydáno v:2007 IEEE Conference on Computer Vision and Pattern Recognition s. 1 - 8
Hlavní autoři: Philbin, J., Chum, O., Isard, M., Sivic, J., Zisserman, A.
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.06.2007
Témata:
Humans
Image retrieval
Information filtering
Information filters
Information retrieval
Large-scale systems
Quantization
Scalability
Silicon
Vocabulary
ISBN:9781424411795, 1424411793
ISSN:1063-6919, 1063-6919
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Shrnutí:In this paper, we present a large-scale object retrieval system. The user supplies a query object by selecting a region of a query image, and the system returns a ranked list of images that contain the same object, retrieved from a large corpus. We demonstrate the scalability and performance of our system on a dataset of over 1 million images crawled from the photo-sharing site, Flickr [3], using Oxford landmarks as queries. Building an image-feature vocabulary is a major time and performance bottleneck, due to the size of our dataset. To address this problem we compare different scalable methods for building a vocabulary and introduce a novel quantization method based on randomized trees which we show outperforms the current state-of-the-art on an extensive ground-truth. Our experiments show that the quantization has a major effect on retrieval quality. To further improve query performance, we add an efficient spatial verification stage to re-rank the results returned from our bag-of-words model and show that this consistently improves search quality, though by less of a margin when the visual vocabulary is large. We view this work as a promising step towards much larger, "web-scale " image corpora.
ISBN:9781424411795
1424411793
ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2007.383172