Abstract
We present in this article a new supervised evaluation criterion that enables the quantification of the quality of region segmentation algorithms. This criterion is compared with seven well-known criteria available in this context. To that end, we test the different methods on natural images by using a subjective evaluation involving different experts from the French community in image processing. Experimental results show the benefit of this new criterion.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Haralick, R.H., Shapiro, L.G.: Image Segmentation Techniques. Image Segmentation Techniques, Computer Vision, Graphics and Image Processing (CVGIP) 29, 100–132 (1985)
Heath, M., Sarkar, S., Sanocki, T., Bowyer, K.: Comparison of Edge Detectors: A Methodology and Initial Study. Computer Vision and Image Understanding (CVIU) 69, 38–54 (1996)
Freixenet, J., Muñoz, X., Raba, D., Marti, J., Cufi, X.: Yet Another Survey on Image Segmentation: Region and Boundary Information Integration. In: Tistarelli, M., Bigun, J., Jain, A.K. (eds.) ECCV 2002. LNCS, vol. 2359, pp. 408–422. Springer, Heidelberg (2002)
Andrey, P.: Selectionist Relaxation: Genetic Algorithms Applied to Image Segmentation. Image and Vision Computing 17, 175–187 (1999)
Bhanu, B., Peng, J.: Adaptative Integrated Image Segmentation and Object Recognition. IEEE transactions on systems, man, and cybernetics 30, 427–441 (2000)
Cavallaro, A., Gelasca, E.D., Ebrahimi, T.: Objective evaluation of segmentation quality using spatio-temporal context. In: IEEE International Conference on Image Processing (ICIP), pp. 301–304. IEEE, Los Alamitos (2002)
Jiang, X., Marti, C., Irniger, C., Bunke, H.: Distance Measures for Image Segmentation Evaluation. EURASIP Journal on Applied Signal Processing 2006, Article ID 35909 (2006)
Zhang, Y.J.: A survey on evaluation methods for image segmentation. Pattern Recognition 29, 1335–1346 (1996)
Chabrier, S., Rosenberger, C., Laurent, H., Emile, B., Marché, P.: Evaluating the segmentation result of a gray-level image. In: European Signal Processing Conference (EUSIPCO), pp. 953–956 (2004)
Montresor, S., Lado, M.J., Tahoces, P.G., Souto, M., Vidal, J.J.: Analytic wavelets applied for the detection of microcalcifications. A tool for digital mammography. In: European Signal Processing Conference (EUSIPCO), pp. 2215–2218 (2004)
Marques, F., Cuberas, G., Gasull, A., Seron, D., Moreso, F., Joshi, N.: Mathematic morphology approach for renal biopsy analysis. In: European Signal Processing Conference (EUSIPCO), pp. 2195–2198 (2004)
Lee, W.W., Richardson, I., Gow, K., Zhao, Y., Staff, R.: Hybrid segmentation of the hippocampus in MR images. In: European Signal Processing Conference (EUSIPCO) (2005)
Chabrier, S., Rosenberger, C., Emile, B.: Evaluation methodologies of image processing: an overview. In: 8th International IEEE Conference on Signal Processing (ICSP), IEEE Computer Society Press, Los Alamitos (2006)
Comaniciu, D., Meer, P.: Mean Shift: A Robust Approach Toward Feature Space Analysis. IEEE Transactions on Pattern analysis and Machine Intelligence 24, 603–619 (2002)
Deng, Y., Manjunath, B.S.: Unsupervised segmentation of color-texture regions in images and video. IEEE Transactions on Pattern Analysis and Machine Intelligence (2001)
Kermad, C., Vozel, B., Chehdi, K.: Hyperspectral image analysis and dimensionality: a scalar scheme through multi-thresholding technique. In: Proceedings of the Eos/Spie Symposium on Remote sensing, vol. 31(4170) (2000)
Vinet, L.: Segmentation et mise en correspondance de régions de paires d’images stéréoscopiques, Thése de Doctorat de l’université de Paris IX Dauphine (1991)
Huang, Q., Dom, B.: Quantitative Methods of Evaluating Image Segmentation. In: Proceedings of the International Conference on Image Processing (ICIP’95), vol. 3, pp. 53–56 (1995)
Yasnoff, W.A., Mui, J.K., Bacus, J.W.: Error measures for scene segmentation. Pattern Recognition 9, 217–231 (1977)
Martin, D., Fowlkes, C., Tal, D., Malik, J.: A Database of Human Segmented Natural Images and its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics. In: Proceedings of the 8th International Conference Computer Vision, pp. 416–423 (2001)
MacQueen, J.B.: Some Methods for classification and Analysis of Multivariate Observations. In: Proceedings of 5-th Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297. University of California Press, Berkeley (1967)
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hafiane, A., Chabrier, S., Rosenberger, C., Laurent, H. (2007). A New Supervised Evaluation Criterion for Region Based Segmentation Methods. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2007. Lecture Notes in Computer Science, vol 4678. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74607-2_40
Download citation
DOI: https://doi.org/10.1007/978-3-540-74607-2_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74606-5
Online ISBN: 978-3-540-74607-2
eBook Packages: Computer ScienceComputer Science (R0)