Using n-grams models for visual semantic place recognition

Mathieu Dubois (1) , Frenoux Emmanuelle (2) , Philippe Tarroux (3)

1 LIMSI - Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur
2 AMI - Architectures et Modèles pour l'Interaction
3 CPU

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.

Mots clés: Semantic place recognition; Hidden Markov models; n-grams

Domaines: Machine Learning [stat.ML]; Vision par ordinateur et reconnaissance de formes [cs.CV]

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