Saliency detection using Multi-layer graph ranking and combined neural networks

In this paper, a new algorithm based on a combined neural network is proposed to improve salient object detection in the complex images. It consists of two main steps. The first step, an objective function which is optimized on a multi-layer graph structure is constructed to diffuse saliency from borders to salient objects, aiming to roughly estimate the location and extent salient objects of an image, meanwhile, color attribute is adopted to rapidly find a set of object-related regions in the image. The second step, establish a combined neural network with Region Net and Local-Global Net. Region Net is adopted to efficiently generate the salient map with the sharp object boundary. Then Local-Global Net based on multi-scale spatial context is proposed to provide strongly reliable multi-scale contextual information, and thus achieves an optimized performance. Experimental results and comparison analysis demonstrate that the proposed algorithm is more effective and superior than most low-level oriented prior methods in terms of precision recall curves, F-measure and mean absolute errors.