Abstract
Image segmentation is an important task in image processing. Nevertheless, there is still no generally accepted quality measure for evaluating the performance of various segmentation algorithms or even different parameterizations of the same algorithm. In this paper, we propose a data fusion-based binary classification framework for image segmentation evaluation. We train and test this framework using a dataset consisting of a variety of image types, their segmentations and respective ground truths, as well as the class labels assigned to each segmentation by human judges. Experimental results show accuracy of up to 80 %.
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Acknowledgements
This work was supported by the National Science Foundation of China under Grant No. 61202190, and the Science and Technology Planning Project of Sichuan Province under Grant No. 2014SZ0207.
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Simfukwe, M., Peng, B., Li, T. (2016). A Data Fusion-Based Framework for Image Segmentation Evaluation. In: Huang, DS., Jo, KH. (eds) Intelligent Computing Theories and Application. ICIC 2016. Lecture Notes in Computer Science(), vol 9772. Springer, Cham. https://doi.org/10.1007/978-3-319-42294-7_48
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DOI: https://doi.org/10.1007/978-3-319-42294-7_48
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