Comparative study of swarm intelligence-based saliency computation

Purpose The purpose of this paper is to propose a new algorithm chaotic pigeon-inspired optimization (CPIO), which can effectively improve the computing efficiency of the basic Itti’s model for saliency-based detection. The CPIO algorithm and relevant applications are aimed at air surveillance for target detection. Design/methodology/approach To compare the improvements of the performance on Itti’s model, three bio-inspired algorithms including particle swarm optimization (PSO), brain storm optimization (BSO) and CPIO are applied to optimize the weight coefficients of each feature map in the saliency computation. Findings According to the experimental results in optimized Itti’s model, CPIO outperforms PSO in terms of computing efficiency and is superior to BSO in terms of searching ability. Therefore, CPIO provides the best overall properties among the three algorithms. Practical implications The algorithm proposed in this paper can be extensively applied for fast, accurate and multi-target detections in aerial images. Originality/value CPIO algorithm is originally proposed, which is very promising in solving complicated optimization problems.