BAG OF WORDS FOR LARGE SCALE OBJECT RECOGNITION - Properties and Benchmark

Mohamed Aly, Mario Munich, Pietro Perona

Abstract

Object Recognition in a large scale collection of images has become an important application of widespread use. In this setting, the goal is to find the matching image in the collection given a probe image containing the same object. In this work we explore the different possible parameters of the bag of words (BoW) approach in terms of their recognition performance and computational cost. We make the following contributions: 1) we provide a comprehensive benchmark of the two leading methods for BoW: inverted file and min-hash; and 2) we explore the effect of the different parameters on their recognition performance and run time, using four diverse real world datasets.

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Paper Citation


in Harvard Style

Aly M., Munich M. and Perona P. (2011). BAG OF WORDS FOR LARGE SCALE OBJECT RECOGNITION - Properties and Benchmark . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2011) ISBN 978-989-8425-47-8, pages 299-306. DOI: 10.5220/0003311402990306


in Bibtex Style

@conference{visapp11,
author={Mohamed Aly and Mario Munich and Pietro Perona},
title={BAG OF WORDS FOR LARGE SCALE OBJECT RECOGNITION - Properties and Benchmark},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2011)},
year={2011},
pages={299-306},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003311402990306},
isbn={978-989-8425-47-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2011)
TI - BAG OF WORDS FOR LARGE SCALE OBJECT RECOGNITION - Properties and Benchmark
SN - 978-989-8425-47-8
AU - Aly M.
AU - Munich M.
AU - Perona P.
PY - 2011
SP - 299
EP - 306
DO - 10.5220/0003311402990306