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Autonomous clustering by fast find of mass and distance peaks

IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第7期47卷 5636-5654页

作者： Yang, Jie Lin, Chin-Teng Australian AI Institute FEIT University of Technology Sydney Computational Intelligence and Brain Computer Interface Lab Australia

Clustering is an essential analytical tool across a wide range of scientific fields, including biology, chemistry, astronomy, and pattern recognition. This paper introduces a novel clustering algorithm as a competitive alternative to existing methods, based on the intuitive principle that a cluster should merge with its nearest neighbor with a higher mass, unless both clusters have relatively large masses and the distance between them is also substantial. By identifying peaks in mass and distance, the algorithm effectively detects and removes incorrect mergers. The proposed method is entirely parameter-free, enabling it to autonomously recognize various cluster types, determine the optimal number of clusters, and identify noise. Extensive experiments on synthetic and real-world data sets demonstrate the algorithm's versatility and consistently strong performance compared to other state-of-the-art methods. © 1979-2012 IEEE.

关键词： Annotations Manuals Detectors Decoding Training Pose Estimation Predictive Models object detection Benchmark Testing Accuracy multi class muti object keypoint detection End To End Model Pose Estimation Scene Analysis Conventional detection keypoint detection detection Boxes Interaction Mechanism object detection Global Features Box Regression One Stage Methods Limited Feedback Image object Training Strategy Bounding Box Training Stage Feed Forward Network multiple objects Pose Estimation Art World Cropped Images Bottom Up Methods Manual Effort object Boxes Top Down Methods Categories Of Persons Crowded Scenes COCO Dataset Human Pose Interactive Segmentation Image Instance Pose Error Res Net 50 Backbone

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