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• W1937315027 abstract "Correlation clustering, or multicut partitioning, is widely used in image segmentation for partitioning an undirected graph or image with positive and negative edge weights such that the sum of cut edge weights is minimized. Due to its NP-hardness, exact solvers do not scale and approximative solvers often give unsatisfactory results. We investigate scalable methods for correlation clustering. To this end we define fusion moves for the correlation clustering problem. Our algorithm iteratively fuses the current and a proposed partitioning which monotonously improves the partitioning and maintains a valid partitioning at all times. Furthermore, it scales to larger datasets, gives near optimal solutions, and at the same time shows a good anytime performance." @default.
• W1937315027 created "2016-06-24" @default.
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• W1937315027 date "2015-06-01" @default.
• W1937315027 modified "2023-10-14" @default.
• W1937315027 title "Fusion moves for correlation clustering" @default.
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• W1937315027 doi "https://doi.org/10.1109/cvpr.2015.7298973" @default.
• W1937315027 hasPublicationYear "2015" @default.
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