Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Searching for objects in an indoor environment can be drastically improved if a task-specific visual saliency is available.  describe a method to incrementally learn such an object-based visual saliency directly on a robot, using an environment exploration mechanism.  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,  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 ll as on publicly available RGB-D images sequences.  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.
['Celine Craye', 'David Filliat', 'Jean-Francois Goudou']
I am writing a research paper on a idea Environment exploration for object-based visual saliency learning
[{"from": "human", "value": "I am writing a research paper on a idea Environment exploration for object-based visual saliency learning"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Searching for objects in an indoor environment can be drastically improved if a task-specific visual saliency is available.  describe a method to incrementally learn such an object-based visual saliency directly on a robot, using an environment exploration mechanism.  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,  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 ll as on publicly available RGB-D images sequences.  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."}]