Extract and Merge: Superpixel Segmentation with Regional Attributes
For a certain object in an image, the relationship between its central region and the peripheral region is not well utilized in existing superpixel segmentation methods. In this work, we propose the concept of regional attribute, which indicates the location of a certain region in the object. Based on the regional attributes, we propose a novel superpixel method called Extract and Merge (EAM). In the extracting stage, we design square windows with a side length of a power of two, named power-window, to extract regional attributes by calculating boundary clearness of objects in the window. The larger windows are for the central regions and the smaller ones correspond to the peripheral regions. In the merging stage, power-windows are merged according to the defined attraction between them. Specifically, we build a graph model and propose an efficient method to make the large windows merge the small ones strategically, regarding power-windows as vertices and the adjacencies between them as edges. We demonstrate that our superpixels have fine boundaries and are superior to the respective state-of-the-art algorithms on multiple benchmarks.
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