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.

中文翻译：

---

显着性装袋：一种用于鲁棒显着对象检测的新框架

显着物体检测是一项具有挑战性的任务，文献中已经提出了几种方法。问题在于大多数方法在特定图像集上表现良好，但在暴露于各种不同图像集时失败。在这里，我们通过提出一种称为显着性装袋的新框架来解决这个问题，该框架以鲁棒的方式在各种图像中检测数字图像中的显着对象。所提出的框架分三个阶段生成图像的显着性图：(i) 选择现有显着性检测模型和生成初始显着图 (ii) 通过应用自适应阈值和多数投票从初始显着图生成集成二值图 (iii) 使用集成二值图和初始显着性计算最终显着图通过应用建议的集成逻辑映射。对六个公开可用的数据集进行了广泛的实验，即。已执行 MSRA10K、DUT-OMRON、ECSSD、PASCAL-S、SED2 和 THUR15K 来确定所提出方法的有效性。所提出方法的性能是根据精度、召回率、F 测量、平均绝对误差 (MAE) 和接收器操作特性 (ROC) 曲线来衡量的，并与 25 种最先进的方法进行了比较，其中包括 17 种经典的最佳方法。过去十年的表演方法，五个现有的选择，和三种聚合显着性方法。所提出的方法在精度、F-Measure 和 MAE 方面优于所有比较的经典和现有选定方法，同时在所有六个数据集的召回率和 ROC 曲线方面与性能最佳的方法相媲美。所提出的框架在计算上比所有比较的聚合方法都快，而在支持其优越性的所有数据集上的性能几乎相同。