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Conference Papers Year : 2016

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 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.
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hal-01289159 , version 1 (17-03-2016)


  • HAL Id : hal-01289159 , version 1


Céline Craye, David Filliat, Jean-François Goudou. Environment Exploration for Object-Based Visual Saliency Learning. International Conference on Robotics and Automation , May 2016, Stockholm, Sweden. ⟨hal-01289159⟩
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