Weighted Intersection over Union (wIoU) for Evaluating Image Segmentation

• URL: http://arxiv.org/abs/2107.09858v5
• Date: Tue, 2 Jul 2024 12:11:39 GMT
• Title: Weighted Intersection over Union (wIoU) for Evaluating Image Segmentation
• Authors: Yeong-Jun Cho,
• Abstract summary: We propose a new evaluation measure called weighted Intersection over Union (wIoU) for semantic segmentation. First, it builds a weight map generated from a boundary distance map, allowing weighted evaluation for each pixel based on a boundary importance factor. We validated the effectiveness of wIoU on a dataset of 33 scenes and demonstrated its flexibility.
• Score: 0.9790236766474198
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, many semantic segmentation methods have been proposed to predict label of pixels in the scene. In general, we measure area prediction errors or boundary prediction errors for comparing methods. However, there is no intuitive evaluation metric that evaluates both aspects. In this work, we propose a new evaluation measure called weighted Intersection over Union (wIoU) for semantic segmentation. First, it builds a weight map generated from a boundary distance map, allowing weighted evaluation for each pixel based on a boundary importance factor. The proposed wIoU can evaluate both contour and region by setting a boundary importance factor. We validated the effectiveness of wIoU on a dataset of 33 scenes and demonstrated its flexibility. Using the proposed metric, we expect more flexible and intuitive evaluation in semantic segmentation field are possible.

Related papers

• Revisiting Evaluation Metrics for Semantic Segmentation: Optimization and Evaluation of Fine-grained Intersection over Union [113.20223082664681]
  We propose the use of fine-grained mIoUs along with corresponding worst-case metrics. These fine-grained metrics offer less bias towards large objects, richer statistical information, and valuable insights into model and dataset auditing. Our benchmark study highlights the necessity of not basing evaluations on a single metric and confirms that fine-grained mIoUs reduce the bias towards large objects.
  arXiv  Detail & Related papers  (2023-10-30T03:45:15Z)
• RankSEG: A Consistent Ranking-based Framework for Segmentation [5.166970737490847]
  We establish a theoretical foundation of segmentation with respect to the Dice/IoU metrics, including the Bayes rule and Dice-/IoU-calibration. We propose a novel consistent ranking-based framework, namely RankDice/RankIoU, inspired by plug-in rules of the Bayes segmentation rule.
  arXiv  Detail & Related papers  (2022-06-27T07:12:31Z)
• PNM: Pixel Null Model for General Image Segmentation [17.971090313814447]
  We present a prior model that weights each pixel according to its probability of being correctly classified by a random segmenter. Experiments on semantic, instance, and panoptic segmentation tasks over three datasets confirm that PNM consistently improves the segmentation quality. We propose a new metric, textitPNM IoU, which perceives the boundary sharpness and better reflects the model segmentation performance in error-prone regions.
  arXiv  Detail & Related papers  (2022-03-13T15:17:41Z)
• Contrastive Boundary Learning for Point Cloud Segmentation [81.7289734276872]
  We propose a novel contrastive boundary learning framework for point cloud segmentation. We experimentally show that CBL consistently improves different baselines and assists them to achieve compelling performance on boundaries.
  arXiv  Detail & Related papers  (2022-03-10T10:08:09Z)
• Boundary IoU: Improving Object-Centric Image Segmentation Evaluation [125.20898025044804]
  We present Boundary IoU, a new segmentation evaluation measure focused on boundary quality. 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.
  arXiv  Detail & Related papers  (2021-03-30T17:59:20Z)
• PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation [96.76882806139251]
  We propose a point-wise affinity propagation module based on the Feature Pyramid Network (FPN) framework, named PointFlow. Rather than dense affinity learning, a sparse affinity map is generated upon selected points between the adjacent features. Experimental results on three different aerial segmentation datasets suggest that the proposed method is more effective and efficient than state-of-the-art general semantic segmentation methods.
  arXiv  Detail & Related papers  (2021-03-11T09:42:32Z)
• Active Boundary Loss for Semantic Segmentation [58.72057610093194]
  This paper proposes a novel active boundary loss for semantic segmentation. It can progressively encourage the alignment between predicted boundaries and ground-truth boundaries during end-to-end training. Experimental results show that training with the active boundary loss can effectively improve the boundary F-score and mean Intersection-over-Union.
  arXiv  Detail & Related papers  (2021-02-04T15:47:54Z)
• Rethinking Semantic Segmentation Evaluation for Explainability and Model Selection [12.786648212233116]
  We introduce a new metric to assess region-based over- and under-segmentation. We analyze and compare it to other metrics, demonstrating that the use of our metric lends greater explainability to semantic segmentation model performance in real-world applications.
  arXiv  Detail & Related papers  (2021-01-21T03:12:43Z)
• Contrastive Rendering for Ultrasound Image Segmentation [59.23915581079123]
  The lack of sharp boundaries in US images remains an inherent challenge for segmentation. We propose a novel and effective framework to improve boundary estimation in US images. Our proposed method outperforms state-of-the-art methods and has the potential to be used in clinical practice.
  arXiv  Detail & Related papers  (2020-10-10T07:14:03Z)
• MetaBox+: A new Region Based Active Learning Method for Semantic Segmentation using Priority Maps [4.396860522241306]
  We present a novel active learning method for semantic image segmentation, called MetaBox+. For acquisition, we train a meta regression model to estimate the segment-wise Intersection over Union (IoU) of each predicted segment of unlabeled images. We compare our method to entropy based methods, where we consider the entropy as uncertainty of the prediction.
  arXiv  Detail & Related papers  (2020-10-05T09:36:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.