Salient Object Detection via Objectness Measure
This work addresses the problem of accurately detecting salient objects in images for applications like image processing, though it appears incremental by building on prior background and contrast methods.
The paper tackled salient object detection by proposing a novel 'foreground connectivity' measure based on objectness proposals to estimate foreground regions, resulting in improved saliency maps that outperform existing state-of-the-art methods on two benchmark databases.
Salient object detection has become an important task in many image processing applications. The existing approaches exploit background prior and contrast prior to attain state of the art results. In this paper, instead of using background cues, we estimate the foreground regions in an image using objectness proposals and utilize it to obtain smooth and accurate saliency maps. We propose a novel saliency measure called `foreground connectivity' which determines how tightly a pixel or a region is connected to the estimated foreground. We use the values assigned by this measure as foreground weights and integrate these in an optimization framework to obtain the final saliency maps. We extensively evaluate the proposed approach on two benchmark databases and demonstrate that the results obtained are better than the existing state of the art approaches.