Watershed-based image segmentation with fast region merging

A hybrid image segmentation algorithm is proposed which combines edge- and region-based techniques through the morphological algorithm of watersheds and consists of the following steps: (a) edge-preserving noise reduction, (b) gradient approximation, (c) detection of watersheds on gradient magnitude image, and (d) hierarchical region merging (HRM) in order to get semantically meaningful segmentations. HRM uses the region adjacency graph (RAG) representation of the image regions. At each step, the most similar pair of regions is determined (minimum cost RAG edge), the regions are merged and the RAG is updated. Traditionally, the above is implemented by storing all the RAG edges in a priority queue (heap). We propose a significantly faster algorithm which maintains an additional graph, the most similar neighbor graph, through which the priority queue size and processing time are drastically reduced. In addition, this region based representation provides one-pixel wide, closed, and accurately localized contours/surfaces. Experimental results using 2D real images are presented