Exploration Strategies for Incremental Learning of Object-Based Visual Saliency

International audienceSearching for objects in an indoor environment can be drastically improved if a task-specific visual saliency is available. We describe a method to learn such an object-based visual saliency in an intrinsically motivated way using an environment exploration mechanism. We first define saliency in a geometrical manner and use this definition to discover salient elements given an attentive but costly observation of the environment. These elements are used to train a fast classifier that predicts salient objects given large-scale visual features. In order to get a better and faster learning, we use intrinsic motivation to drive our observation selection, based on uncertainty and novelty detection. Our approach has been tested on RGB-D images, is real-time, and outperforms several state-of-the-art methods in the case of indoor object detection

Apprentissage incrémental de la saillance visuelle pour des applications robotique

National audienceNous proposons une méthode d'apprentissage incrémental de la saillance visuelle par un mécanisme d'exploration de l'environnement. Partant d'une définition géométrique de la saillance des objets, notre système observe de façon attentive et ciblée son environnement, jusqu'à découvrir des éléments saillants. Un classifieur permet alors d'apprendre les caractéristiques visuelles correspondantes afin de pouvoir ensuite prédire rapidement les positions des objets sans analyse géométrique. Notre approche a été testée sur des images RGBD, fonctionne en temps réel et dépasse plusieurs méthodes de l'état de l'art sur le contexte particulier de la détection d'objets en intérieur

On the Use of Intrinsic Motivation for Visual Saliency Learning

International audienceThe use of intrinsic motivation for the task of learning sensori-motor properties has received a lot of attention over the last few years, but only little work has been provided toward using intrinsic motivation for the task of learning visual signals. In this paper, we propose to apply the main ideas of the Intelligent Adaptive Curiosity (IAC) for the task of visual saliency learning. We here present RL-IAC, an adapted version of IAC that uses reinforcement learning to deal with time consuming displacements while actively learning saliency based on local learning progress. We also introduce the use of a backward evaluation to deal with a learner that is shared between several regions. We demonstrate the good performance of RL-IAC compared to other exploration techniques, and we discuss the performance of other intrinsic motivation sources instead of learning progress in our problem

RL-IAC: An Exploration Policy for Online Saliency Learning on an Autonomous Mobile Robot

International audienceIn the context of visual object search and localization, saliency maps provide an efficient way to find object candidates in images. Unlike most approaches, we propose a way to learn saliency maps directly on a robot, by exploring the environment, discovering salient objects using geometric cues, and learning their visual aspects. More importantly, we provide an autonomous exploration strategy able to drive the robot for the task of learning saliency. For that, we describe the Reinforcement Learning-Intelligent Adaptive Curiosity algorithm (RL-IAC), a mechanism based on IAC (Intelligent Adaptive Curiosity) able to guide the robot through areas of the space where learning progress is high, while minimizing the time spent to move in its environment without learning. We demonstrate first that our saliency approach is an efficient tool to generate relevant object boxes proposal in the input image and significantly outperforms state-of-the-art algorithms. Second, we show that RL-IAC can drastically decrease the required time for learning saliency compared to random exploration

Exploring to learn visual saliency: The RL-IAC approach

International audienceThe problem of object localization and recognition on autonomous mobile robots is still an active topic. In this context, we tackle the problem of learning a model of visual saliency directly on a robot. This model, learned and improved on-the-fly during the robot's exploration provides an efficient tool for localizing relevant objects within their environment. The proposed approach includes two intertwined components. On the one hand, we describe a method for learning and incrementally updating a model of visual saliency from a depth-based object detector. This model of saliency can also be exploited to produce bounding box proposals around objects of interest. On the other hand, we investigate an autonomous exploration technique to efficiently learn such a saliency model. The proposed exploration, called Reinforcement Learning-Intelligent Adaptive Curiosity (RL-IAC) is able to drive the robot's exploration so that samples selected by the robot are likely to improve the current model of saliency. We then demonstrate that such a saliency model learned directly on a robot outperforms several state-of-the-art saliency techniques, and that RL-IAC can drastically decrease the required time for learning a reliable saliency model

BioVision: a Biomimetics Platform for Intrinsically Motivated Visual Saliency Learning

International audienceWe present BioVision, a bio-mimetics platform based on the human visual system. BioVision relies on the foveal vision principle based on a set of cameras with wide and narrow fields of view. We present in this platform a mechanism for learning visual saliency in an intrinsically motivated fashion. This model of saliency, learned and improved on-the-fly during the robot's exploration provides an efficient tool for localizing relevant objects within their environment. The proposed approach includes two intertwined components. On the one hand, a method for learning and incrementally updating a model of visual saliency from foveal observations. On the other hand, we investigate an autonomous exploration technique to efficiently learn such a saliency model. The proposed exploration, based on the IAC (Intelligent Adaptive Curiosity) algorithm is able to drive the robot's exploration so that samples selected by the robot are likely to improve the current model of saliency. We then demonstrate that such a saliency model learned directly on a robot outperforms several state-of-the-art saliency techniques, and that IAC can drastically decrease the required time for learning a reliable saliency model. We also investigate the behavior of IAC in a non static environment, and how well this algorithm can adapt to changes

Environment Exploration for Object-Based Visual Saliency Learning

International audienceSearching for objects in an indoor environment can be drastically improved if a task-specific visual saliency is available. We describe a method to incrementally learn such an object-based visual saliency directly on a robot, using an environment exploration mechanism. We first define saliency based on a geometrical criterion and use this definition to segment salient elements given an attentive but costly and restrictive observation of the environment. These elements are used to train a fast classifier that predicts salient objects given large-scale visual features. In order to get a better and faster learning, we use an exploration strategy based on intrinsic motivation to drive our attentive observation. Our approach has been tested on a robot in our lab as well as on publicly available RGB-D images sequences. We demonstrate that the approach outperforms several state-of-the-art methods in the case of indoor object detection and that the exploration strategy can drastically decrease the time required for learning saliency