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Class Specific Segmentation

In this work we address the task of learning how to segment a particular class of objects, by means of a training set of images and their segmentations. In particular we propose a  method to overcome the extremely high training time of a previously proposed solution to this problem, Kernelized Structural Support Vector Machines.We employ a one-class SVM working with joint kernels to robustly learn significant support vectors (representative image-mask pairs) and accordingly weight them to build a suitable energy function for the graph cut framework. We report results obtained on two public datasets and a comparison of training times on different training set sizes.


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Publications

1 M. Manfredi; C. Grana; R. Cucchiara "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 Conference
2 M. Manfredi; C. Grana; R. Cucchiara "Learning Graph Cut Energy Functions for Image Segmentation" Proceedings of the 22nd International Conference on Pattern Recognition, Stockholm, Sweden, pp. 960 -965 , Aug. 24-28, 2014 | DOI: 10.1109/ICPR.2014.175 Conference

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