Using n-grams Models for Visual Semantic Place Recognition

Mathieu Dubois, Emmanuelle Frenoux, Philippe Tarroux

2013

Abstract

The aim of this paper is to present a new method for visual place recognition. Our system combines global image characterization and visual words, which allows to use efficient Bayesian filtering methods to integrate several images. More precisely, we extend the classical HMM model with techniques inspired by the field of Natural Language Processing. This paper presents our system and the Bayesian filtering algorithm. The performance of our system and the influence of the main parameters are evaluated on a standard database. The discussion highlights the interest of using such models and proposes improvements.

Paper Citation

in Harvard Style

Dubois M., Frenoux E. and Tarroux P. (2013). Using n-grams Models for Visual Semantic Place Recognition . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013) ISBN 978-989-8565-47-1, pages 808-813. DOI: 10.5220/0004298708080813

in Bibtex Style

@conference{visapp13,
author={Mathieu Dubois and Emmanuelle Frenoux and Philippe Tarroux},
title={Using n-grams Models for Visual Semantic Place Recognition},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013)},
year={2013},
pages={808-813},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004298708080813},
isbn={978-989-8565-47-1},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013)
TI - Using n-grams Models for Visual Semantic Place Recognition
SN - 978-989-8565-47-1
AU - Dubois M.
AU - Frenoux E.
AU - Tarroux P.
PY - 2013
SP - 808
EP - 813
DO - 10.5220/0004298708080813