Robot semantic place recognition based on deep belief networks and a direct use of tiny images
type: Dataset
description: Usually, human beings are able to quickly distinguish between different places, solely from their visual appearance. This is due to the fact that they can organize their space as composed of discrete units. These units, called ``semantic places'', are characterized by their spatial extend and their functional unity. Such a semantic category can thus be used as contextual information which fosters object detection and recognition. Recent works in semantic place recognition seek to endow the robot with similar capabilities. Contrary to classical localization and mapping works, this problem is usually addressed as a supervised learning problem. The question of semantic places recognition in robotics - the ability to recognize the semantic category of a place to which scene belongs to - is therefore a major requirement for the future of autonomous robotics. It is indeed required for an autonomous service robot to be able to recognize the environment in which it lives and to easily learn the organization of this environment in order to operate and interact successfully. To achieve that goal, different methods have been already proposed, some based on the identification of objects as a prerequisite to the recognition of the scenes, and some based on a direct description of the scene characteristics. If we make the hypothesis that objects are more easily recognized when the scene in which they appear is identified, the second approach seems more suitable. It is however strongly dependent on the nature of the image descriptors used, usually empirically derived from general considerations on image coding.Compared to these many proposals, another approach of image coding, based on a more theoretical point of view, has emerged the last few years. Energy-based models of feature extraction based on the principle of minimizing the energy of some function according to the quality of the reconstruction of the image has lead to the Restricted Boltzmann Machines (RBMs) able to code a…
landing page: https://theses.hal.science/tel-00960289
identifier: NNT: 2012PA112298
keyword: architecture-profonde
keyword: bag-of-words
keyword: deep-belief-networks
keyword: energy-based-models
keyword: infoeu-reposemanticsdoctoralthesis
keyword: infoinfo-ohcomputer-science-csother-csoh
keyword: machine-de-boltzmann-restreinte
keyword: modeles-bases-sur-lenergie
keyword: reconnaissance-de-lieux-semantiques
keyword: regression-softmax
keyword: restrict-ed-boltzmann-machines
keyword: sac-de-mots
keyword: semantic-places-recognition
keyword: softmax-regression
keyword: theses
issued: 2026-05-06T00:31:53.219336
modified: 2026-05-06T00:31:53.219341
language: en
contact point: Organization
  fn: CCSD

test_moissonnage_selune
type: Agent

Robot semantic place recognition based on deep belief networks and a direct use of tiny images
type: Distribution
format: HTML
access URL: https://theses.hal.science/tel-00960289
issued: 2026-05-06T00:31:53.254049
modified: 2026-05-06T00:31:53.202712