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This desirable general separability criterion should take larger values when the within-class scatter is smaller and when the between-class scatter is larger

The covariance of all projected samples defines the mixture scatter matrixS M ∈R L−1×L−1, which naturally decomposes into within-, between-class terms: SM = 1 n−1 nX i=1 (yi −

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Deep Image Segmentation via Discriminant Feature Learning

cs.CV · 2026-05-14 · unverdicted · novelty 6.0

Deep Discriminant Analysis (DDA) is a new loss that maximizes between-class variance and minimizes within-class variance to produce more compact and separable features for image segmentation.

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  • Deep Image Segmentation via Discriminant Feature Learning cs.CV · 2026-05-14 · unverdicted · none · ref 5

    Deep Discriminant Analysis (DDA) is a new loss that maximizes between-class variance and minimizes within-class variance to produce more compact and separable features for image segmentation.