Adaptive background search and foreground estimation for saliency detection via comprehensive autoencoder
Ke Yan, Changyang Li, Xiuying Wang, Ang Li, Yuchen Yuan, Jinman Kim, Dagan D. Feng · 2016
In saliency object detection, inappropriate boundary-background priors is known to degrade performance in challenging image datasets, and even may lead to `inverse' results when saliency regions are attached to the image boundaries. This is an active field where many works have proposed various techniques to lessen such degradation by inappropriate boundary-background priors. Although the use of boundary-background priors has shown to be capable of improving the detection, inherently, these techniques confront serious challenges in background suppression. To overcome this limitation, we propose an adaptive background extractor to search background seeds without the need of boundary-background priors. With the adaptive background seeds, the saliency objects can be then extracted via our proposed hierarchical foreground estimation model. We evaluate our adaptive Background Search and Foreground Estimation (BSFE) algorithm in comparison with six state-of-the-art methods on four well-recognized public datasets. The experimental results demonstrate that our BSFE algorithm outperforms compared methods in majority of the datasets and in particular achieves double-winners in terms of F-measure and mean absolute error on two challenging datasets.