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Image Matching with Distinctive Visual Vocabulary

Hongwen Kang, Martial Hebert and Takeo Kanade
Conference Paper, Carnegie Mellon University, IEEE Workshop on Applications of Computer Vision (WACV) 2011, January, 2011

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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.

BibTeX Reference
@conference{Kang-2011-7209,
title = {Image Matching with Distinctive Visual Vocabulary},
author = {Hongwen Kang and Martial Hebert and Takeo Kanade},
booktitle = {IEEE Workshop on Applications of Computer Vision (WACV) 2011},
keyword = {Image matching, distinctiveness, visual vocabulary},
sponsor = {National Science Foundation},
school = {Robotics Institute , Carnegie Mellon University},
month = {January},
year = {2011},
address = {Pittsburgh, PA},
}
2017-09-13T10:40:28+00:00