Autonomous Incremental Visual Environment Perception Based on Visual Selective Attention

Recognition regarding the changing environment is an essential role for survival. Novelty scene detection plays an important role in evoking self motivation to adapt to changing environments and efficiently to bring about new knowledge. In this paper, we propose a biologically motivated novelty scene detection model, which is implemented by a proposed incremental computation model. Every input scene is represented by visual scan path topology and the energy signatures, which are obtained from a saliency map generated by a low level top-down visual attention model in conjunction with a bottom-up saliency map model. The obtained representation for an input scene is used as the input for the incremental computation model in order to memorize scenes and detect novelty scenes. The computer experimental results show that the proposed model successfully indicates a novelty for natural color input scenes in a natural visual environment.