Deep Image Segmentation via Discriminant Feature Learning

2026-05-14

AI Summary

This work addresses the limitations of existing image segmentation methods, which often produce blurry boundaries and low-confidence predictions due to loss functions that neglect the discriminative structure of features. To overcome this, we propose a differentiable, architecture-agnostic Deep Discriminant Analysis (DDA) loss that integrates classical discriminant analysis principles into end-to-end training. The DDA loss explicitly maximizes inter-class variance while minimizing intra-class variance, thereby encouraging the learning of compact and well-separated feature representations. Notably, it incurs no additional inference overhead and can be seamlessly incorporated into any segmentation network. Extensive experiments on the DIS5K benchmark demonstrate that DDA consistently enhances segmentation accuracy, boundary sharpness, and model confidence, confirming the effectiveness of discriminative feature learning for image segmentation.

Abstract

Accurate image segmentation remains challenging, particularly in generating sharp, confident boundaries. While modern architectures have advanced the field, many of them still rely on standard loss functions like Cross-Entropy and Dice, which often neglect the discriminative structure of learned features, leading to inaccurate boundaries. This work introduces Deep Discriminant Analysis (DDA), a differentiable, architecture-agnostic loss function that embeds classical discriminant principles for network training. DDA explicitly maximizes between-class variance while minimizing within-class one, promoting compact and separable feature distributions without increasing inference cost. Evaluations on the DIS5K benchmark demonstrate that DDA consistently improves segmentation accuracy, boundary sharpness, and model confidence across various architectures. Our results show that integrating discriminant analysis offers a simple, effective path for building more robust segmentation models.

Problem

• image segmentation
• boundary accuracy
• discriminant feature learning
• loss function
• feature distribution

Innovation

• Deep Discriminant Analysis
• image segmentation
• discriminant feature learning
• boundary sharpness
• loss function