NDLI: Total Recall: Automatic Query Expansion with a Generative Feature Model for Object Retrieval

Content Provider	IEEE Xplore Digital Library
Author	Chum, O. Philbin, J. Sivic, J. Isard, M. Zisserman, A.
Copyright Year	2007
Description	Author affiliation: Univ. of Oxford, Oxford (Chum, O.; Philbin, J.; Sivic, J.)
Abstract	Given a query image of an object, our objective is to retrieve all instances of that object in a large (1M+) image database. We adopt the bag-of-visual-words architecture which has proven successful in achieving high precision at low recall. Unfortunately, feature detection and quantization are noisy processes and this can result in variation in the particular visual words that appear in different images of the same object, leading to missed results. In the text retrieval literature a standard method for improving performance is query expansion. A number of the highly ranked documents from the original query are reissued as a new query. In this way, additional relevant terms can be added to the query. This is a form of blind rele- vance feedback and it can fail if 'outlier' (false positive) documents are included in the reissued query. In this paper we bring query expansion into the visual domain via two novel contributions. Firstly, strong spatial constraints between the query image and each result allow us to accurately verify each return, suppressing the false positives which typically ruin text-based query expansion. Secondly, the verified images can be used to learn a latent feature model to enable the controlled construction of expanded queries. We illustrate these ideas on the 5000 annotated image Oxford building database together with more than 1M Flickr images. We show that the precision is substantially boosted, achieving total recall in many cases.
Starting Page	1
Ending Page	8
File Size	1541164
Page Count	8
File Format	PDF
ISBN	9781424416301
ISSN	15505499
DOI	10.1109/ICCV.2007.4408891
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-10-14
Publisher Place	Brazil
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Information retrieval Image retrieval Feedback Image databases Feature extraction Vocabulary Data mining Solid modeling Geometry Data engineering
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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