GCUT: Segmentation Using Normalized Graph Cut Algorithm
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The paper introduces "GCUT: Segmentation by Normalized Graph Cut," which represents an image segmentation approach. Specifically, this algorithm treats an image as a graph where each pixel corresponds to a node, and the similarity between pixels defines edge weights between nodes. The segmentation is performed by applying normalized graph cut operations based on graph boundary information. In code implementation, this typically involves constructing an affinity matrix using Gaussian similarity functions, computing the normalized Laplacian matrix, and performing eigenvalue decomposition to partition the graph. This method finds extensive applications in image processing fields, including object detection and image segmentation tasks. The algorithm's implementation often utilizes functions for graph construction, weight calculation, and spectral clustering techniques to achieve optimal partitioning results.
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