Saliency generation from complex scene via digraph and Bayesian inference

Making a close inspection of the recent progress made in computer vision we will find that more and more advancement can be attributed to the introduction of bio-inspired algorithms, which is a flourishing area of computing. Biological patterns can be generated from salient attention principle of the nervous system, which pops out the most important information from large backgrounds. New evidences of the underlying neuronal mechanisms indicate that biological motion phenomena happen on higher cognition level instead of low-level pixels in sensing environment, especially in a complex scene. Inspired by it, this paper proposes a novel saliency detection method based on a directed graph model and multi-scale Bayesian inference. We first create a directed graph with superpixels as its nodes and introduce a baseline node whose saliency is considered to be zero. The saliency of each node is defined as the shortest distance from the baseline node to it and Dijkstra׳s algorithm is adjusted to solve this optimization problem with great efficiency. Furthermore, we extend this model to a multi-scale version to cope with salient regions of different sizes and a Bayesian inference strategy via 3D color histogram is developed to achieve pixel-level saliency. Experimental results on some benchmark dataset demonstrate the superiority of our method with respect to 18 state-of-the-art saliency detection methods and our method achieves the highest recall in MSRA-1000. Additional experiments on ship detection of SAR images show that the proposed method can overcome the shortcomings of traditional CFAR detector and has much fewer false alarms in cluttered background.