Medoid Shift: An Unsupervised Clustering Algorithm for Image Segmentation
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Resource Overview
Medoid Shift is an unsupervised clustering algorithm, similar to k-means but applicable to broader fields. It can be used for image segmentation and other applications, with a MATLAB implementation provided. The source code offers insights into key functions like distance computation and medoid updating, making it accessible for researchers and developers.
Detailed Documentation
In this article, we introduce Medoid Shift, an unsupervised clustering algorithm. Similar to k-means, Medoid Shift has broader applicability and can be effectively used for image segmentation. The algorithm is implemented in MATLAB, and the source code is provided to facilitate understanding and application by researchers and developers. Key implementation aspects include distance matrix calculation and iterative medoid updates using MATLAB's built-in functions. Additionally, Medoid Shift can be applied to other domains such as anomaly detection and data compression, demonstrating wide-ranging potential.
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