A MultiScale Particle Filter Framework for Contour Detection

We investigate the contour detection task in complex natural images. We propose a novel contour detection algorithm which jointly tracks at two scales small pieces of edges called edgelets. This multiscale edgelet structure naturally embeds semi-local information and is the basic element of the proposed recursive Bayesian modeling. Prior and transition distributions are learned offline using a shape database. Likelihood functions are learned online, thus are adaptive to an image, and integrate color and gradient information via local, textural, oriented, and profile gradient-based features. The underlying model is estimated using a sequential Monte Carlo approach, and the final soft contour detection map is retrieved from the approximated trajectory distribution. We also propose to extend the model to the interactive cut-out task. Experiments conducted on the Berkeley Segmentation data sets show that the proposed MultiScale Particle Filter Contour Detector method performs well compared to competing state-of-the-art methods.