Background-Driven Salient Object Detection

• Published in: IEEE transactions on multimedia 2017-04, Vol.19 (4), p.750-762
• Main Authors: Wang, Zilei, Xiang, Dao, Hou, Saihui, Wu, Feng
• Format: Article
• Language: English
• Subjects: background prior; Benchmark testing; Computational modeling; Estimation; graph-based optimization; Image color analysis; Object detection; Optimization; Robustness; saliency estimation; Salient object detection
• ISSN: 1520-9210; 1941-0077
• DOI: 10.1109/TMM.2016.2636739

The background information is a significant prior for salient object detection, especially when images contain cluttered background and diverse object parts. In this paper, we propose a background-driven salient object detection (BD-SOD) method to more comprehensively exploit the background prior, aiming at generating more accurate and robust salient maps. To be specific, we first exploit the background prior to conduct the saliency estimation, i.e., computing the regional saliency values. In this stage, the background prior is utilized in threefold: restricting the reference regions to only the background regions, weighting the contribution of reference regions, and leveraging the importance of different features. Benefiting from such an explicit utilization, the proposed model can greatly mitigate the negative interference of the cluttered background and diverse object parts. We then embed the background prior into the optimization graph for saliency refinement. Specifically, two virtual supernodes (representing the background and foreground, respectively) are introduced with extra connections, and the nonlocal feature connections between similar regions are also set up. These connections enhance the power of optimization graph to alleviate the perturbations from diverse parts, and thus help to achieve the uniformity of saliency values. Finally, we provide systematical studies to investigate the effectiveness of the proposed BD-SOD in exploiting the valuable background prior. Experimental results on multiple public benchmark datasets, including MSRA-1000, THUS-10000, PASCAL-S, and ECSSD, clearly show that BD-SOD consistently outperforms the well-established baselines and achieves state-of-the-art performance.