Abstract
This paper focuses on a fundamental problem in computer vision: how to evaluate the quality of image segmentation. Supervised evaluation methods provide a more accurate evaluation than the unsupervised methods, but these methods cannot work without manually-segmented reference segmentations. This shortcoming limits its applications. We present an edge-based evaluation method which works without the comparison with reference segmentations. Our method evaluates the quality of segmentation by three edge-based measures: the edge fitness, the intra-region edge error and the out-of-bound error. Experimental results show that our method provides a more accurate evaluation than those method based on the statistic of pixel values, and can be used in both segmentation evaluation and region evaluation. A significant linear correlation is shown between the evaluation scores of our method and two widely used supervised methods. The proposed methods show a high performance on the automatic choice of the best fitted parameters for region growing.
This work is supported by National Natural Science Foundation of China (61370102, 61170193, 61203310, 61370185), Guangdong Natural Science Foundation (2014A030306050, S2013010013432, S2012020011081, S2013010015940), the Ministry of Education - China Mobile Research Funds (MCM20130331), the Fundamental Research Funds for the Central Universities, SCUT (2014ZG0043,2015PT022) and The Pearl River Science & technology Star Project (2012J2200007).
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Zhang, H., Fritts, J.E., Goldman, S.A.: Image segmentation evaluation: a survey of unsupervised methods. Computer Vision and Image Understanding 110(2), 260–280 (2008)
Malisiewicz, T., Efros, A.A.: Improving spatial support for objects via multiple segmentations (2007)
Unnikrishnan, R., Pantofaru, C., Hebert, M.: Toward objective evaluation of image segmentation algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(6), 929–944 (2007)
Meila, M.: Comparing clusterings: an axiomatic view. In: Proceedings of the 22nd International Conference on Machine Learning (ICML-05), pp. 577–584 (2005)
Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. IEEE Transactions onPattern Analysis and Machine Intelligence 33(5), 898–916 (2011)
Zhang, H., Fritts, J.E., Goldman, S.A.: An entropy-based objective evaluation method for image segmentation. In: Electronic Imaging 2004, pp. 38–49. International Society for Optics and Photonics (2003)
Chabrier, S., Emile, B., Rosenberger, C., Laurent, H.: Unsupervised performance evaluation of image segmentation. EURASIP Journal on Applied Signal Processing 2006, 217–217 (2006)
Chen, H.C., Wang, S.J.: The use of visible color difference in the quantitative evaluation of color image segmentation. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, Proceedings. (ICASSP 2004), vol. 3, pp. iii-593. IEEE (2004)
Carreira, J., Sminchisescu, C.: Cpmc: automatic object segmentation using constrained parametric min-cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence 34(7), 1312–1328 (2012)
Kim, T.H., Lee, K.M., Lee, S.U.: Learning full pairwise affinities for spectral segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(7), 1690–1703 (2013)
Donoser, M., Schmalstieg, D.: Discrete-continuous gradient orientation estimation for faster image segmentation. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3158–3165. IEEE (2014)
Maire, M., Arbeláez, P., Fowlkes, C., Malik, J.: Using contours to detect and localize junctions in natural images. In: IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2008, pp. 1–8. IEEE (2008)
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Liang, Y., Huang, H., Cai, Z. (2015). Edge-Based Unsupervised Evaluation of Image Segmentation. In: Zha, H., Chen, X., Wang, L., Miao, Q. (eds) Computer Vision. CCCV 2015. Communications in Computer and Information Science, vol 546. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48558-3_27
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DOI: https://doi.org/10.1007/978-3-662-48558-3_27
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