Saliency bagging: a novel framework for robust salient object detection

Abstract

Salient object detection is a challenging task, and several methods have been proposed for the same in the literature. The problem lies in that most of the methods perform good on a particular set of images but fail when exposed to a variety of different set of images. Here, we address this problem by proposing a novel framework called saliency bagging for detecting salient object(s) in digital images across a variety of images in a robust manner. The proposed framework generates the saliency map of an image in three phases: (i) Selection of existing saliency detection models and generation of initial saliency maps (ii) Generation of integrated binary map from the initial saliency maps by applying adaptive thresholding and majority voting (iii) Computation of final saliency map using integrated binary map and initial saliency maps by applying proposed integration logic. Extensive experiments on six publicly available datasets viz. MSRA10K, DUT-OMRON, ECSSD, PASCAL-S, SED2, and THUR15K have been performed to determine the effectiveness of the proposed method. The performance of the proposed method is measured in terms of Precision, Recall, F-Measure, Mean Absolute Error (MAE) and Receiver Operating Characteristic (ROC) curve and compared with 25 state-of-the-art methods including 17 classic best-performing methods of the last decade, five existing selected, and three aggregation saliency methods. The proposed method outperforms all the compared classic and existing selected methods in terms of Precision, F-Measure, and MAE, while it is comparable to the best-performing methods in terms of Recall and ROC curve across all the six datasets. The proposed framework is computationally very fast than all compared aggregation methods, while performance is almost same on all datasets that support its superiority.