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Abstract

In this paper we propose an image indexing and matching algorithm that relies on selecting distinctive high dimensional features. In contrast with conventional techniques that treated all features equally, we claim that one can benefit significantly from focusing on distinctive features. We propose a bag-of-words algorithm that combines the feature distinctiveness in visual vocabulary generation. Our approach compares favorably with the state of the art in image matching tasks on the University of Kentucky Recognition Benchmark dataset and on an indoor localization dataset. We also show that our approach scales up more gracefully on a large scale Flickr dataset.

Keywords

Image matching, distinctiveness, visual vocabulary

Notes

Sponsor: National Science Foundation

Associated Center(s) / Consortia: Vision and Autonomous Systems Center and Quality of Life Technology Center

Associated Lab(s) / Group(s): Human-Robot Interaction Group

Associated Project(s): Quality of Life Technology

Text Reference

Hongwen Kang, Martial Hebert, and Takeo Kanade, "Image Matching with Distinctive Visual Vocabulary," IEEE Workshop on Applications of Computer Vision (WACV) 2011, January, 2011.

BibTeX Reference

@inproceedings{Kang_2011_6756,
   author = "Hongwen Kang and Martial Hebert and Takeo Kanade",
   title = "Image Matching with Distinctive Visual Vocabulary",
   booktitle = "IEEE Workshop on Applications of Computer Vision (WACV) 2011",
   month = "January",
   year = "2011",
}

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