Using n-grams models for visual semantic place recognition - Laboratoire Interdisciplinaire des Sciences du Numérique Access content directly
Conference Papers Year : 2013

Using n-grams models for visual semantic place recognition

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.
Fichier principal
Vignette du fichier
visapp2013.pdf (104.51 Ko) Télécharger le fichier
poster.pdf (1.01 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Format : Other

Dates and versions

hal-00962167 , version 1 (21-03-2014)

Identifiers

Cite

Mathieu Dubois, Frenoux Emmanuelle, Philippe Tarroux. Using n-grams models for visual semantic place recognition. VISAPP, INSTICC, Feb 2013, Barcelona, Spain. ⟨hal-00962167⟩
116 View
177 Download

Altmetric

Share

Gmail Facebook X LinkedIn More