Non-rigid Segmentation Using Sparse Low Dimensional Manifolds and Deep Belief Networks

In this paper, we propose a new methodology for segmenting non-rigid visual objects, where the search procedure is onducted directly on a sparse low-dimensional manifold, guided by the classification results computed from a deep belief network. Our main contribution is the fact that we do not rely on the typical sub-division of segmentation tasks into rigid detection and non-rigid delineation. Instead, the non-rigid segmentation is performed directly, where points in the sparse low-dimensional can be mapped to an explicit contour representation in image space. Our proposal shows significantly smaller search and training complexities given that the dimensionality of the manifold is much smaller than the dimensionality of the search spaces for rigid detection and non-rigid delineation aforementioned, and that we no longer require a two-stage segmentation process. We focus on the problem of left ventricle endocardial segmentation from ultrasound images, and lip segmentation from frontal facial images using the extended Cohn-Kanade (CK+) database. Our experiments show that the use of sparse low dimensional manifolds reduces the search and training complexities of current segmentation approaches without a significant impact on the segmentation accuracy shown by state-of-the-art approaches.