An Efficient and Fast Active Contour Model for Salient Object Detection

In this paper, we investigate the polarity information to improve the active contour model proposed by Chunming et al. Unlike the traditional level set formulations, the variational level set formulation proposed by forces the level set function to be close to a signed distance function,and therefore completely eliminates the need of the reinitialization procedure and speeds up the curve evolution.However, like the majority of classical active contour models,the model proposed by relies on a gradient based stopping function, depending on the image gradient, to stop the curve evolution. Consequently, using gradient information for noisy and textured images, the evolving curve may pass through or stop far from the salient object boundaries.Moreover, in this case, the isotropic smoothing Gaussian has to be strong, which will smooth the edges too. For these reasons, we propose the use of a polarity based stopping function. In fact, comparatively to the gradient information,the polarity information accurately distinguishes the boundaries or edges of the salient objects. Hence, combining the polarity information with the active contour model of we obtain a fast and efficient active contour model for salient object detection. Experiments are performed on several images to show the advantage of the polarity based active contour.