Superpixel segmentation has become an important research problem in image processing. In this paper, we propose an Iterative Spanning Forest (ISF) framework, based on sequences of Image Foresting Transforms, where one can choose i) a seed sampling strategy, ii) a connectivity function, iii) an adjacency relation, and iv) a seed pixel recomputation procedure to generate improved sets of connected superpixels (supervoxels in 3D) per iteration. The superpixels in ISF structurally correspond to spanning trees rooted at those seeds. We present five ISF methods to illustrate different choices of its components. These methods are compared with approaches from the state-of-the-art in effectiveness and efficiency. The experiments involve 2D and 3D datasets with distinct characteristics, and a high level application, named sky image segmentation. The theoretical properties of ISF are demonstrated in the supplementary material and the results show that some of its methods are competitive with or superior to the best baselines in effectiveness and efficiency.
A first method based on the ISF framework appeared in @cite_2 and has been successfully used in a high level application @cite_22 . It is considered in our experiments.
@cite_22 @cite_2
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First-order methods have been popularly used for solving large-scale problems. However, many existing works only consider unconstrained problems or those with simple constraint. In this paper, we develop two first-order methods for constrained convex programs, for which the constraint set is represented by affine equations and smooth nonlinear inequalities. Both methods are based on the classic augmented Lagrangian function. They update the multipliers in the same way as the augmented Lagrangian method (ALM) but employ different primal variable updates. The first method, at each iteration, performs a single proximal gradient step to the primal variable, and the second method is a block update version of the first one. For the first method, we establish its global iterate convergence as well as global sublinear and local linear convergence, and for the second method, we show a global sublinear convergence result in expectation. Numerical experiments are carried out on the basis pursuit denoising and a convex quadratically constrained quadratic program to show the empirical performance of the proposed methods. Their numerical behaviors closely match the established theoretical results. The first Clef-Ip test collection was made available in 2009 to support research in IR methods in the intellectual property domain; only one type of retrieval task Prior Art Search was given to the participants. Since then the test collection has been extended with both more content and varied types of tasks, reflecting various specific parts of patent experts' workflows. In 2013 we organized two tasks --- Passage Retrieval Starting from Claims and Structure Recognition --- on which we report in this work. This paper proposes a method to combine text-based and citation-based retrieval methods in the invalidity patent search. Using the NTCIR-6 test collection including eight years of USPTO patents, we show the effectiveness of our method experimentally. In this paper, we adapt the heuristic of Fortz and Thorup for optimizing the weights of Shortest Path First protocols suchas Open Shortest Path First (OSPF) or Intermediate System-Intermediate System (IS-IS), in order to take into account failurescenarios.More precisely, we want to find a set of weights that is robust to all single link failures. A direct application of the originalheuristic, evaluating all the link failures, is too time consuming for realistic networks, so we developed a method based on acritical set of scenarios aimed to be representative of the whole set of scenarios. This allows us to make the problem manageableand achieve very robust solutions.