Learning Superpixel Relations for Supervised Image Segmentation
Abstract: In this paper we propose to extend the well known graph cut segmentation framework by learning superpixel relations and use them to weight superpixel-to-superpixel edges in a superpixel graph. Adjacent superpixel-pairs are analyzed to build an object boundary model, able to discriminate between superpixel-pairs belonging to the same object or placed on the edge between the foreground object and the background. Several superpixel-pair features are investigated and exploited to build a non-linear SVM to learn object boundary appearance. The adoption of this modified graph cut enhances the performance of a previously proposed segmentation method on two publicly available datasets, reaching state-of-the-art results.
Citation:
Manfredi, Marco; Grana, Costantino; Cucchiara, Rita "Learning Superpixel Relations for Supervised Image Segmentation" Proceedings of the 21st International Conference on Image Processing, Paris, France, pp. 4437 -4441 , Oct. 27-30, 2014 DOI: 10.1109/ICIP.2014.7025900
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- DOI: 10.1109/ICIP.2014.7025900