Heterogeneous Stacking for Classification-Driven Watershed Segmentation

Abstract

Marker-driven watershed segmentation attempts to extract seeds that indicate the presence of objects within an image. These markers are subsequently used to enforce regional minima within a topological surface used by the watershed algorithm. The classification-driven watershed segmentation (CDWS) algorithm improved the production of markers and topological surface by employing two machine-learned pixel classifiers. The probability maps produced by the two classifiers were utilized for creating markers, object boundaries, and the topological surface. This paper extends the CDWS algorithm by (i) enabling automated feature extraction via independent components analysis and (ii) improving the segmentation accuracy by introducing heterogeneous stacking. Heterogeneous stacking, an extension of stacked generalization for object delineation, improves pixel labeling and segmentation by training base classifiers on multiple target concepts extracted from the original ground truth, which are subsequently fused by the second set of classifiers. Experimental results demonstrate the effectiveness of the proposed system on real world images, and indicate significant improvement in segmentation quality over the base system.