Salient object detection using biogeography-based optimization to combine features

Salient object detection aims to automatically localize a foreground object with respect to its background in an image. It plays a crucial role in a wide range of computer vision and multimedia applications. In this work, we propose an improved salient object detection method based on biogeography-based optimization, a relatively new bio-inspired metaheuristic algorithm that searches for the global optimum using a migration model. Our approach consists of two steps. In the first step, a set of local (multi-scale contrast), regional (center-surround histogram), and global (color spatial distribution) salient feature maps are extracted and normalized. In the second step, an optimal weight vector for combining these feature maps into one saliency map is determined using biogeography-based optimization and improved variants of this algorithm. As a result, a salient objects were identified and labeled as distinct from the image background. We implemented our method using three biogeography-based optimization variants, and compared our results for three popular databases against two other state-of-the-art approaches. The experimental results demonstrate that our method exhibits refined and consistent detection of salient objects.