Oscillation analysis for salient object detection

Salient object detection from an image is important for many multimedia applications. Existing methods provide good solutions to saliency detection; however, their results often emphasize the high-contrast edges, instead of regions/objects. In this paper, we present a method for salient object detection based on oscillation analysis. Our study shows that salient objects and their backgrounds have different amplitudes of oscillation between the local minima and maxima. Based on this observation, our method analyzes the oscillation in an image by estimating its local minima and maxima and computes the saliency map according to the oscillation magnitude contrast. Our method detects the local minima and maxima and performs extreme interpolation to smoothly propagate these information to the whole image. In this way, the oscillation information is smoothly assigned to regions, retaining well-defined salient boundaries as there are large variations near the salient boundaries (edges between objects and their backgrounds). As a result, our saliency map highlights salient regions/objects instead of high-contrast boundaries. We experiment with our method on two large public data set. Our results demonstrate the effectiveness of our method. We further apply our salient object detection method to automatic salient object segmentation, which again shows the success.