Visual Saliency Detection Using a Rule-Based Aggregation Approach

Abstract

In this paper, we propose an approach for salient pixel detection using a rule-based system. In our proposal, rules are automatically learned by combining four saliency models. The learned rules are utilized for the detection of pixels of the salient object in a visual scene. The proposed methodology consists of two main stages. Firstly, in the training stage, the knowledge extracted from outputs of four state-of-the-art saliency models is used to induce an ensemble of rough-set-based rules. Secondly, the induced rules are utilized by our system to determine, in a binary manner, the pixels corresponding to the salient object within a scene. Being independent of any threshold value, such a method eliminates any midway uncertainty and exempts us from performing a post-processing step as is required in most approaches to saliency detection. The experimental results on three datasets show that our method obtains stable and better results than state-of-the-art models. Moreover, it can be used as a pre-processing stage in computer vision-based applications in diverse areas such as robotics, image segmentation, marketing, and image compression.