Boosting image retrieval

We present an approach for image retrieval using a very large number of highly selective features and efficient online learning. Our approach is predicated on the assumption that each image is generated by a sparse set of visual "causes" and that images which are visually similar share cau...

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Bibliographic Details
Main Authors: Tieu, K., Viola, P.
Format: Conference Proceeding
Language:English
Subjects:
Artificial intelligence
Boosting
Electrical capacitance tomography
Humans
Image databases
Image retrieval
Information retrieval
Laboratories
Learning
Spatial databases
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Summary:We present an approach for image retrieval using a very large number of highly selective features and efficient online learning. Our approach is predicated on the assumption that each image is generated by a sparse set of visual "causes" and that images which are visually similar share causes. We propose a mechanism for computing a very large number of highly selective features which capture some aspects of this causal structure (in our implementation there are over 45,000 highly selective features). At query time a user selects a few example images, and a technique known as "boosting" is used to learn a classification function in this feature space. By construction, the boosting procedure learns a simple classifier which only relies on 20 of the features. As a result a very large database of images can be scanned rapidly, perhaps a million images per second. Finally we will describe a set of experiments performed using our retrieval system on a database of 3000 images.
ISSN:1063-6919
DOI:10.1109/CVPR.2000.855824