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 an image as the superposition of a limited number of features taken from a larger alphabet. It has also been shown that this process can be repeated in a deep architecture, leading to a sparse and efficient representation of the initial data in the feature space. A complex problem of classification in the input space is thus transformed into an easier one in the feature space. This approach has been successfully applied to the identification of tiny images from the 80 millions image database of the MIT. In the present work, we demonstrate that semantic place recognition can be achieved on the basis of tiny images instead of conventional Bag-of-Word (BoW) methods and on the use of Deep Belief Networks (DBNs) for image coding. We show that after appropriate coding a softmax regression in the projection space is sufficient to achieve promising classification results. To our knowledge, this approach has not yet been investigated for scene recognition in autonomous robotics. We compare our methods with the state-of-the-art algorithms using a standard database of robot localization. We study the influence of system parameters and compare different conditions on the same dataset. These experiments show that our proposed model, while being very simple, leads to state-of-the-art results on a semantic place recognition task.

Il est généralement facile pour les humains de distinguer rapidement différents lieux en se basant uniquement sur leur aspect visuel. . Ces catégories sémantiques peuvent être utilisées comme information contextuelle favorisant la détection et la reconnaissance d'objets. Des travaux récents en reconnaissance des lieux visent à doter les robots de capacités similaires. Contrairement aux travaux classiques, portant sur la localisation et la cartographie, cette tâche est généralement traitée comme un problème d'apprentissage supervisé.La reconnaissance de lieux sémantiques - la capacité à reconnaître la catégorie sémantique à laquelle une scène appartient – peut être considérée comme une condition essentielle en robotique autonome. Un robot autonome doit en effet pouvoir apprendre facilement l'organisation sémantique de son environnement pour pouvoir fonctionner et interagir avec succès. Pour atteindre cet objectif, différentes méthodes ont déjà été proposées. Certaines sont basées sur l'identification des objets comme une condition préalable à la reconnaissance des scènes, et d'autres fondées sur une description directe des caractéristiques de la scène. Si nous faisons l'hypothèse que les objets sont plus faciles à reconnaître quand la scène dans laquelle ils apparaissent est bien identifiée, la deuxième approche semble plus appropriée. Elle est cependant fortement dépendante de la nature des descripteurs d'images utilisées qui sont généralement dérivés empiriquement a partir des observations générales sur le codage d'images.En opposition avec ces propositions, une autre approche de codage des images, basée sur un point de vue plus théorique, a émergé ces dernières années. Les modèles d'extraction de caractéristiques fondés sur le principe de la minimisation d'une fonction d'énergie en relation avec un modèle statistique génératif expliquant au mieux les données, ont abouti à l'apparition des Machines de Boltzmann Restreintes (Rectricted Boltzmann Machines : RBMs) capables de coder une image comme la superposition d'un nombre limité de caractéristiques extraites à partir d'un plus grand alphabet. Il a été montré que ce processus peut être répété dans une architecture plus profonde, conduisant à une représentation parcimonieuse et efficace des données initiales dans l'espace des caractéristiques. Le problème complexe de la classification dans l'espace de début est ainsi remplacé par un problème plus simple dans l'espace des caractéristiques.Dans ce travail, nous montrons que la reconnaissance sémantiques des lieux peut être réalisée en considérant des mini-images au lieu d'approches plus classiques de type ''sacs-de-mots'' et par l'utilisation de réseaux profonds pour le codage des images. Après avoir realisé un codage approprié, une régression softmax dans l'espace de projection est suffisante pour obtenir des résultats de classification prometteurs. A notre connaissance, cette approche n'a pas encore été proposée pour la reconnaissance de scène en robotique autonome.Nous avons comparé nos méthodes avec les algorithmes de l'état-de-l'art en utilisant une base de données standard de localisation de robot. Nous avons étudié l'influence des paramètres du système et comparé les différentes conditions sur la même base de données. Les expériences réalisées montrent que le modèle que nous proposons, tout en étant très simple, conduit à des résultats comparables à l'état-de-l'art sur une tâche de reconnaissance de lieux sémantiques. / 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 an image as the superposition of a limited number of features taken from a larger alphabet. It has also been shown that this process can be repeated in a deep architecture, leading to a sparse and efficient representation of the initial data in the feature space. A complex problem of classification in the input space is thus transformed into an easier one in the feature space. This approach has been successfully applied to the identification of tiny images from the 80 millions image database of the MIT. In the present work, we demonstrate that semantic place recognition can be achieved on the basis of tiny images instead of conventional Bag-of-Word (BoW) methods and on the use of Deep Belief Networks (DBNs) for image coding. We show that after appropriate coding a softmax regression in the projection space is sufficient to achieve promising classification results. To our knowledge, this approach has not yet been investigated for scene recognition in autonomous robotics. We compare our methods with the state-of-the-art algorithms using a standard database of robot localization. We study the influence of system parameters and compare different conditions on the same dataset. These experiments show that our proposed model, while being very simple, leads to state-of-the-art results on a semantic place recognition task.