A lightweight weakly supervised learning segmentation algorithm for imbalanced image based on rotation density peaks

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摘要

Image segmentation has been an important technique in the field of image processing. Fine-level manual annotations are very limited and difficult for a large collection of imbalanced images, where each image contains quite different small objects. However, we find that these imbalanced images have similar decision graphs obtained by a lightweight and simple clustering algorithm Density Peaks (DPeaks). Hence, in this paper, we propose a weakly supervised image segmentation algorithm. It trains a decision curve from decision graphs of a few sample imbalanced images by SVM and Support Vector Regression (SVR), which is effective for identifying the sparse region of an imbalanced image. Besides, RangeTree is applied to accelerate RDP for large images due to the high complexity of DPeaks Clustering. Experiments prove that the proposed algorithm works well on imbalanced image data sets, not only it can recognize main things, but also has the ability to identify some relatively small objects.

论文关键词:Image segmentation,Density peaks clustering,Clustering,Lightweight weakly supervised learning,Machine learning

论文评审过程:Received 26 November 2021, Revised 24 February 2022, Accepted 26 February 2022, Available online 7 March 2022, Version of Record 27 March 2022.

论文官网地址:https://doi.org/10.1016/j.knosys.2022.108513