Face and Hair Region Labeling Using Semi-Supervised Spectral Clustering-Based Multiple Segmentations

The multiple segmentation (MS) scheme is considered to be a way to get a better spatial support for various shaped objects in image segmentation. The MS scheme assumes that the segmented regions (i.e., segments) can be treated as hypotheses for object support rather than mere partitionings of the image. As for attaining each segmentation in the MS scheme, one of the most popular methods is to employ spectral clustering (SC). When applied to image segmentation tasks, SC groups a set of pixels or small regions into unique segments. While it has been popularly used in image segmentation, it often fails to deal with images containing objects with complex boundaries. To split the image as close to the object boundaries as possible, some prior knowledge can be used to guide the clustering algorithm toward appropriate partitioning of the data. In semisupervised clustering, prior knowledge is often formulated as pairwise constraints. In this paper, we propose an MS technique combined with constrained SC to build a face and hair region labeler. To put it concretely, pairwise constraints modified to fit the problem of labeling face regions are added to SC and multiple segments are generated by the constrained SC. Then, the labeling is conducted by estimating the likelihoods for each segment to belong to the target object classes. Experiments are conducted on three datasets and the results show that the proposed scheme offers useful tools for labeling the face images.