Classification-Driven Watershed Segmentation-IEEE TRANSACTIONS ON IMAGE PROCESSING,
VOL. 16, NO. 5, MAY 2007 – java


Abstract—This paper presents a novel approach for creation of topographical function and object markers used within watershed segmentation. Typically, marker-driven watershed segmentation extracts seeds indicating the presence of objects or background at specific image locations. The marker locations are then set to be regional minima within the topological surface (typically, the gradient of the original input image), and the watershed algorithm is applied. In contrast, our approach uses two classifiers, one trained to produce markers, the other trained to produce object boundaries.
As a result of using machine-learned pixel classification, the
proposed algorithm is directly applicable to both single channel
and multichannel image data. Additionally, rather than flooding
the gradient image, we use the inverted probability map produced
by the second aforementioned classifier as input to the watershed
algorithm. Experimental results demonstrate the superior performance
of the classification-driven watershed segmentation algorithm
for the tasks of 1) image-based granulometry and 2) remote
sensing.