Superpixel Segmentation by Object-Based Iterative Spanning Forest
Superpixel segmentation methods aim at representing image objects by the union of connected regions (superpixels). Such aim can be better approximated with a higher number of superpixels per object, which often leads to an unnecessary over-segmentation due to the absence of prior object information. In this work, we extend the Iterative Spanning Forest (ISF) framework to include object information and present a superpixel segmentation method based on object saliency detection. As ISF, the new framework, named Object-based ISF (OISF), relies on multiple executions of the Image Foresting Transform (IFT) algorithm for improved seed sets, such that each seed defines one connected superpixel as a spanning tree rooted at that seed. We describe an IFT-based method for object saliency detection and show that the corresponding saliency maps can improve seed estimation and connectivity function, increasing the superpixel resolution inside a given object. Experimental results on two medical image datasets demonstrate that the proposed OISF-based method outperforms the state-of-the-art in boundary adherence with higher number of superpixels inside the object.
KeywordsSuperpixels Object saliency map Image Foresting Transform
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