Environment exploration for object based visual saliency learning
Searching 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 displacement in order to get relevant observations. Our approach has been tested on a robot in indoor environments 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.
2016 IEEE International Conference on Robotics and Automation (ICRA)