Using n-grams models for visual semantic place recognition
This work addresses visual place recognition for robotics or computer vision applications, but it appears incremental as it builds on existing HMM and NLP methods.
The paper tackles visual place recognition by combining global image characterization and visual words with Bayesian filtering, extending HMM models using NLP-inspired techniques, and evaluates performance on a standard database.
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.