Fusion of Measures for Image Segmentation Evaluation
- https://doi.org/10.2991/ijcis.2019.125905654How to use a DOI?
- Image segmentation evaluation; Data fusion; Discrete Cosine Transform; Classifier model
Image segmentation is an important task in image processing. However, no universally accepted quality scheme exists for evaluating the performance of various segmentation algorithms or just different parameterizations of the same algorithm. In this paper, an extension of a fusion-based framework for evaluating image segmentation quality is proposed. This framework uses supervised image segmentation evaluation measures as features. These features are combined together and used to train and test a number of classifiers. Preliminary results for this framework, using seven evaluation measures, were reported with an accuracy rate of 80%. In this study, ten image segmentation evaluation measures are used, nine of which have already been proposed in literature. Moreover, one novel measure is proposed, based on the Discrete Cosine Transform (DCT), and is thus named the DCT metric. Before applying it in the fusion-based framework, the DCT metric is first compared with some state-of-the-art evaluation measures. Experimental results demonstrate that the DCT metric outperforms some existing measures. The extended fusion-based framework for image segmentation evaluation proposed in the study outperforms the original fusion-based framework, with an accuracy rate of 86% and a large Kappa value equal to 0.72. Hence, the novelty in this paper is in two aspects: firstly, the DCT metric and secondly, the extension of the fusion-based framework for evaluation of image segmentation quality.
- © 2019 The Authors. Published by Atlantis Press SARL.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - JOUR AU - Macmillan Simfukwe AU - Bo Peng AU - Tianrui Li PY - 2019 DA - 2019/01/28 TI - Fusion of Measures for Image Segmentation Evaluation JO - International Journal of Computational Intelligence Systems SP - 379 EP - 386 VL - 12 IS - 1 SN - 1875-6883 UR - https://doi.org/10.2991/ijcis.2019.125905654 DO - https://doi.org/10.2991/ijcis.2019.125905654 ID - Simfukwe2019 ER -