Saliency-based scene recognition based on growing competitive neural network

This paper proposes the saliency-based scene recognition model in which objects in saliency-based attended spots are sequentially encoded to be invariant with respect to position and size and their positions and sizes are encoded simultaneously. In this model, object recognition and its recall are performed based on the growing two-layered competitive spiking neural network with reciprocal connection between the layers. This neural network represents objects using latency-based temporal coding and grows in size and recognizability through learning and self-organization. Through simulation experiments of a robot equipped with a camera, it is shown that scene recognition is well performed by our model, in which objects are encoded in-variantly with respect to position and size and their positions and sizes are encoded suitably enough for scene recognition.