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Cluster Detection and Centers of Mass Prediction in Images
- 1 Nanjing Foreign Language School, Nanjing, 210008, China
- 2 Department of Computer Science, Miami University, Ohio state, Oxford City,45056, United States
- 3 Department of Art & Science, Case Western Reserve University, Cleveland,44106, United States
- 4 Tabor Academy, Marion,02738, United States
* Author to whom correspondence should be addressed.
Abstract
This paper provides an exhaustive evaluation of different algorithms for the accurate detection and localization of dense clusters on complex image data with a resolution to maintain real centroids in obscure, overlapping, or densely packed situations. Our approach differs by its use of the local maximum algorithm to quickly identify potential cluster centers (by detecting local maxima in pixel intensity) as a first step. The algorithm starts by initializing cluster centers, which are then refined using the k-Means clustering method that iteratively relocates the positions of these clusters until an optimal configuration is reached. We tighten our approach by adding custom loss functions to Convolutional Neural Networks (CNNs) that encode the predicted number of clusters and their center positions. An early stopping mechanism is introduced to mitigate the issues of noise and overfitting, with performance measured against a test set. Furthermore, we examine the average pooling and center-of-gravity approaches for noise reduction to predict cluster centers without unnecessary outliers. This combination, along with the Hungarian algorithm, which is used to optimally match predicted and real centers, has shown better results in detecting the correct number of clusters across different datasets. These validation results show that our combined method effectively addresses challenges related to overlapping clusters, increasing robustness and accuracy in identification. The approach seems to have potential for applications in high-precision image segmentation and analysis.
Keywords
Cluster Detection, Local Maxima Algorithm, k-Means Algorithm, Average Pooling, Convolutional Neural Networks
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Cite this article
Xu,H.;Wang,Y.;Hou,S.;Wei,S. (2025). Cluster Detection and Centers of Mass Prediction in Images. Applied and Computational Engineering,131,40-51.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 2nd International Conference on Machine Learning and Automation
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