Local saliency consistency-based label inference for weakly supervised salient object detection using scribble annotations

Recently, weak supervision has received growing attention in the field of salient object detection due to the convenience of labelling. However, there is a large performance gap between weakly supervised and fully supervised salient object detectors because the scribble annotation can only provide very limited foreground/background information. Therefore, an intuitive idea is to infer annotations that cover more complete object and background regions for training. To this end, a label inference strategy is proposed based on the assumption that pixels with similar colours and close positions should have consistent labels. Specifically, k-means clustering algorithm was first performed on both colours and coordinates of original annotations, and then assigned the same labels to points having similar colours with colour cluster centres and near coordinate cluster centres. Next, the same annotations for pixels with similar colours within each kernel neighbourhood was set further. Extensive experiments on six benchmarks demonstrate that our method can significantly improve the performance and achieve the state-of-the-art results.