Novel biological visual attention mechanism via Gaussian harmony search

Most of the visual attention models are based on the concept of a two-dimensional saliency map, which encodes the conspicuity of the object in the visual scene. The visual attention model proposed by Laurent Itti is used in this work. In Itti's model, the saliency map is calculated via combining the information across several modalities, including color, intensity, and orientation. In this work, we propose a pre-training process to select the weightings used in the combining of feature maps to make the target more conspicuity in the saliency map. Harmony search (HS) algorithm is used in the pre-training process to obtain the weightings. HS is a new heuristic algorithm, which mimics the improvisation of music players. Its performance has been verified by many benchmark problems. We modify the pitch adjustment process of the original HS to improve the optimization performance and accelerate the convergence rate. The modified algorithm is named Gaussian harmony search (GHS).