Nguyen Tam V, Nguyen Khanh, Do Thanh-Toan
IEEE Trans Image Process. 2019 Jan 23. doi: 10.1109/TIP.2019.2894284.
Salient object detection aims to detect the main objects in the given image. In this paper, we proposed an approach that integrates semantic priors into the salient object detection process. The method first obtains an explicit saliency map that is refined by the explicit semantic priors learned from data. Then an implicit saliency map is constructed using a trained model that maps the implicit semantic priors embedded into superpixel features with the saliency values. Next, the fusion saliency map is computed by adaptively fusing both the explicit and implicit semantic maps. The final saliency map is eventually computed via the post-processing refinement step. Experimental results have demonstrated the effectiveness of the proposed method, particularly, it achieves competitive performance with the state-of-the-art baselines on three challenging datasets, namely, ECSSD, HKUIS, and iCoSeg.
显著目标检测旨在检测给定图像中的主要物体。在本文中，我们提出了一种将语义先验整合到显著目标检测过程中的方法。该方法首先获得一个由从数据中学到的显式语义先验细化的显式显著图。然后，使用一个训练模型构建一个隐式显著图，该模型将嵌入超像素特征中的隐式语义先验与显著值进行映射。接下来，通过自适应融合显式和隐式语义图来计算融合显著图。最终的显著图最终通过后处理细化步骤来计算。实验结果证明了所提方法的有效性，特别是在三个具有挑战性的数据集，即ECSSD、HKUIS和iCoSeg上，它与当前最先进的基线相比具有有竞争力的性能。
