Efficient Learning for Discriminative Segmentation with Supermodular LossesReport as inadecuate

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1 GALEN - Organ Modeling through Extraction, Representation and Understanding of Medical Image Content Inria Saclay - Ile de France, Ecole Centrale Paris 2 CVN - Centre de vision numérique 3 Departement Elektrotechniek - ESAT leuven

Abstract : Several supermodular losses have been shown to improve the perceptual quality of image segmentation in a discriminative framework such as a structured output support vector machine SVM. These loss functions do not necessarily have the same structure as the segmentation inference algorithm, and in general, we may have to resort to generic submodular minimization algorithms for loss augmented inference. Although these come with polynomial time guarantees, they are not practical to apply to image scale data. Many supermodular losses come with strong optimization guarantees, but are not readily incorporated in a loss augmented graph cuts procedure. This motivates our strategy of employing the alternating direction method of multipliers ADMM decomposition for loss augmented inference. In doing so, we create a new API for the structured SVM that separates the maximum a posteriori MAP inference of the model from the loss augmentation during training. In this way, we gain computational efficiency, making new choices of loss functions practical for the first time, while simultaneously making the inference algorithm employed during training closer to the test time procedure. We show improvement both in accuracy and computational performance on the Microsoft Research Grabcut database and a brain structure segmentation task, empirically validating the use of a supermodular loss during training, and the improved computational properties of the proposed ADMM approach over the Fujishige-Wolfe minimum norm point algorithm.

Author: Jiaqian Yu - Matthew Blaschko -

Source: https://hal.archives-ouvertes.fr/


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