Boundary IoU: Improving Object-Centric Image Segmentation Evaluation (2103.16562v1)

Abstract: We present Boundary IoU (Intersection-over-Union), a new segmentation evaluation measure focused on boundary quality. We perform an extensive analysis across different error types and object sizes and show that Boundary IoU is significantly more sensitive than the standard Mask IoU measure to boundary errors for large objects and does not over-penalize errors on smaller objects. The new quality measure displays several desirable characteristics like symmetry w.r.t. prediction/ground truth pairs and balanced responsiveness across scales, which makes it more suitable for segmentation evaluation than other boundary-focused measures like Trimap IoU and F-measure. Based on Boundary IoU, we update the standard evaluation protocols for instance and panoptic segmentation tasks by proposing the Boundary AP (Average Precision) and Boundary PQ (Panoptic Quality) metrics, respectively. Our experiments show that the new evaluation metrics track boundary quality improvements that are generally overlooked by current Mask IoU-based evaluation metrics. We hope that the adoption of the new boundary-sensitive evaluation metrics will lead to rapid progress in segmentation methods that improve boundary quality.

• The paper introduces Boundary IoU as a novel metric that emphasizes boundary errors over traditional pixel-based measures in segmentation tasks.
• It employs a fixed pixel distance approach to evaluate boundary alignment, offering symmetry and sensitivity across varied object sizes.
• Empirical results demonstrate that integrating Boundary IoU into AP and PQ metrics yields more precise evaluations for applications like autonomous driving and augmented reality.

Boundary IoU: Enhancing Image Segmentation Evaluation

The paper introduces Boundary IoU, a novel evaluation metric designed to address the limitations of traditional segmentation measures, primarily focusing on boundary quality for object-centric image segmentation. This work is essential given the increasing need for precise boundary delineations in various computer vision applications.

Core Contributions

1. Boundary IoU Metric: Boundary IoU is proposed as a more sensitive alternative to the conventional Mask IoU for evaluating boundary quality in image segmentations. Traditional Mask IoU, while effective in assessing segmentation accuracy, tends to undervalue boundary errors in larger objects due to its all-encompassing pixel consideration. Boundary IoU instead focuses on IoU within a fixed pixel distance from both predicted and ground truth boundaries. This shift allows the metric to measure boundary alignment more accurately, especially in larger objects where previous methods falter.
2. Sensitivity and Symmetry: Compared to existing boundary-focused metrics, Boundary IoU offers symmetry concerning prediction and ground truth pairs and balanced responsiveness across object scales, addressing the shortcomings of measures like Trimap IoU and F-measure. These measures either lack symmetry or fail to respond adequately to errors across varying object sizes.
3. Updated Segmentation Metrics: Building on Boundary IoU, the authors propose Boundary AP (Average Precision) for instance segmentation and Boundary PQ (Panoptic Quality) for panoptic segmentation. These metrics integrate Boundary IoU into existing framework structures to capture improvements in boundary quality that were previously overlooked.
4. Empirical Validation: The paper validates the efficacy of Boundary IoU through extensive sensitivity analysis across different error types and object sizes, demonstrating its superior sensitivity to boundary quality. It shows that Boundary AP and Boundary PQ capture segmentation improvements across scales more effectively than their mask-based counterparts.

Implications

The introduction of Boundary IoU and its derivatives has both theoretical and practical implications. Theoretically, it challenges the prevailing complacency in segmentation quality evaluation, emphasizing the necessity of boundary accuracy. Practically, it offers a refined toolkit for measuring progress in segmentation methods, particularly in applications requiring high-fidelity boundary delineations, such as augmented reality and autonomous driving.

Future Directions

Looking forward, the adoption of boundary-sensitive evaluation metrics could spur advancements in segmentation methods, possibly inspiring new architectures or refined loss functions specifically targeting boundary quality. Further research could explore the integration of Boundary IoU in optimization loops or leverage its properties to enhance model training, thereby improving boundary-aware segmentations in real-time applications.

Boundary IoU represents a methodical improvement in the evaluation landscape of image segmentation. It challenges researchers to consider boundary quality improvements in their segmentation models and provides a robust metric to quantify such advancements. This paper lays a strong foundation for future developments in object-centric image segmentation evaluation, significantly impacting both academic research and industrial practice.