Boosting Image Retrieval

Abstract

We present an approach for image retrieval using a very large number of highly selective features and efficient learning of queries. 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 46,000 highly selective features). At query time a user selects a few example images, and the AdaBoost algorithm is used to learn a classification function which depends on a small number of the most appropriate features. This yields a highly efficient classification function. In addition we show that the AdaBoost framework provides a natural mechanism for the incorporation of relevance feedback. Finally we show results on a wide variety of image queries.

Title

Boosting Image Retrieval

Journal

International Journal of Computer Vision
Volume 56, Issue 1-2 , pp 17-36

Cover Date

2004-01-01

DOI

10.1023/B:VISI.0000004830.93820.78

Print ISSN

0920-5691

Online ISSN

1573-1405

Publisher

Kluwer Academic Publishers

Additional Links

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• Manuscript Submission

Topics

• Artificial Intelligence (incl. Robotics)
• Computer Imaging, Graphics and Computer Vision
• Image Processing
• Automation and Robotics

Keywords

• image database
• sparse representation
• feature selection
• relevance feedback

Industry Sectors

• IT & Software
• Electronics
• Engineering
• Aerospace
• Telecommunications
• Automotive
• Consumer Packaged Goods

Authors

• Kinh Tieu ⁽¹⁾
• Paul Viola ⁽²⁾ 

Author Affiliations

• 1. Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
• 2. Mitsubishi Electric Research Labs, Cambridge, MA, 02139, USA