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• W4387446373 abstract "Random feature maps attempt to approximate the kernel method with low computational complexity, and they are efficient and effective algorithms for dealing with the non-linear structure of data. Nevertheless, the existing random feature-based methods are sensitive to hyper-parameters, and their performances are affected by the randomness in feature maps. To improve the robustness of random feature maps and further speed up kernel-based image segmentation algorithms, a multiple random feature superpixel-based fuzzy clustering is proposed in this paper. First, we generate a group of random features from the original image via a novel multiple random feature scheme. Then, we use the superpixel segmentation to reduce the scale of multiple random features. The final segmentation results are obtained by performing the multi-view fuzzy clustering on the multiple random feature superpixels. Experiments on two shared benchmark data sets verify the superiority of our approach." @default.
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• W4387446373 date "2023-06-02" @default.
• W4387446373 modified "2023-10-11" @default.
• W4387446373 title "Multiple Random Feature Superpixel-Based Fuzzy Clustering for Image Segmentation" @default.
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• W4387446373 doi "https://doi.org/10.1109/iccss58421.2023.10270406" @default.
• W4387446373 hasPublicationYear "2023" @default.
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