Abstract
In this work we propose to use ordered directionally monotone functions to build an image feature extractor. Some theoretical aspects about directional monotonicity are studied to achieve our goal and a construction method for an image application is presented. Our proposal is compared to well-known methods in the literature as the gravitational method, the fuzzy morphology or the Canny method, and shows to be competitive. In order to improve the method presented, we propose a consensus feature extractor using combinations of the different methods. To this end we use ordered weighted averaging aggregation functions and obtain a new feature extractor that surpasses the results obtained by state-of-the-art methods.
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
Similar content being viewed by others
References
Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 33(5), 898–916 (2011)
Beliakov, G., Pradera, A., Calvo, T.: Aggregation Functions: A Guide for Practitioners, vol. 18. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-73721-6
Bezdek, J., Chandrasekhar, R., Attikouzel, Y.: A geometric approach to edge detection. IEEE Trans. Fuzzy Syst. 6(1), 52–75 (1998)
Bustince, H., Barrenechea, E., Sesma-Sara, M., Lafuente, J., Dimuro, G.P., Mesiar, R., Kolesarova, A.: Ordered directionally monotone functions. Justification and application. IEEE Trans. Fuzzy Syst. PP(99), 1 (2017)
Bustince, H., Fernandez, J., Kolesárová, A., Mesiar, R.: Directional monotonicity of fusion functions. Eur. J. Oper. Res. 244, 300–308 (2015)
Calvo, T., Kolesárová, A., Komorníková, M., Mesiar, R.: Aggregation operators: properties, classes and construction methods. In: Calvo, T., Mayor, G., Mesiar, R. (eds.) Aggregation Operators: New Trends and Applications. STUDFUZ, vol. 97, pp. 3–104. Springer, Heidelberg (2002). https://doi.org/10.1007/978-3-7908-1787-4_1
Canny, J.F.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 679–698 (1986)
Estrada, F.J., Jepson, A.D.: Benchmarking image segmentation algorithms. Int. J. Comput. Vis. 85(2), 167–181 (2009)
Forero-Vargas, M.G.: Fuzzy thresholding and histogram analysis. In: Nachtegael, M., Van der Weken, D., Kerre, E.E., Van De Ville, D. (eds.) Fuzzy Filters for Image Processing. STUDFUZZ, vol. 122, pp. 129–152. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-540-36420-7_6
Gonzalez-Hidalgo, M., Massanet, S., Mir, A., Ruiz-Aguilera, D.: On the choice of the pair conjunction-implication into the fuzzy morphological edge detector. IEEE Trans. Fuzzy Syst. 23(4), 872–884 (2015)
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. Int. J. Comput. Vis. 1(4), 321–331 (1988)
Law, T., Itoh, H., Seki, H.: Image filtering, edge detection, and edge tracing using fuzzy reasoning. IEEE Trans. Pattern Anal. Mach. Intell. 18(5), 481–491 (1996)
Lopez-Molina, C., Bustince, H., Fernandez, J., Couto, P., De Baets, B.: A gravitational approach to edge detection based on triangular norms. Pattern Recognit. 43(11), 3730–3741 (2010)
Lopez-Molina, C., De Baets, B., Bustince, H.: A framework for edge detection based on relief functions. Inf. Sci. 278, 127–140 (2014)
Marr, D., Hildreth, E.: Theory of edge detection. Proc. R. Soc. Lond. B: Biol. Sci. 207(1167), 187–217 (1980)
Martin, D.R., Fowlkes, C.C., Malik, J.: Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Trans. Pattern Anal. Mach. Intell. 26(5), 530–549 (2004)
Medina-Carnicer, R., Madrid-Cuevas, F.J., Carmona-Poyato, A., Muñoz-Salinas, R.: On candidates selection for hysteresis thresholds in edge detection. Pattern Recognit. 42(7), 1284–1296 (2009)
Medina-Carnicer, R., Muñoz-Salinas, R., Yeguas-Bolivar, E., Diaz-Mas, L.: A novel method to look for the hysteresis thresholds for the Canny edge detector. Pattern Recognit. 44(6), 1201–1211 (2011)
Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12(7), 629–639 (1990)
Prewitt, J.: Object enhancement and extraction (1970)
Schweiser, B., Sklar, A.: Associative functions and statistical triangle inequalities. Publ. Math. Debr. 8, 169–186 (1961)
Sobel, I., Feldman, G.: A 3x3 isotropic gradient operator for image processing. In: Hart, P.E., Duda, R.O. (eds.) Pattern Classification and Scene Analysis, pp. 271–272. Wiley, Hoboken (1973)
Torre, V., Poggio, T.: On edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(2), 147–163 (1986)
Wilkin, T., Beliakov, G.: Weakly monotonic averaging functions. Int. J. Intell. Syst. 30(2), 144–169 (2015)
Yager, R.: Quantifier guided aggregation using OWA operators. Int. J. Intell. Syst. 11(1), 49–73 (1996)
Yager, R.R.: On ordered weighted averaging aggregation operators in multicriteria decisionmaking. IEEE Trans. Syst. Man Cybern. 18(1), 183–190 (1988)
Acknowledgments
This work is supported by the Spanish Ministry of Science (Project TIN2016-77356-P) and the Research Services of Universidad Publica de Navarra.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Marco-Detchart, C., Dimuro, G.P., Sesma-Sara, M., Castillo-Lopez, A., Fernandez, J., Bustince, H. (2018). Consensus Image Feature Extraction with Ordered Directionally Monotone Functions. In: Barreto, G., Coelho, R. (eds) Fuzzy Information Processing. NAFIPS 2018. Communications in Computer and Information Science, vol 831. Springer, Cham. https://doi.org/10.1007/978-3-319-95312-0_14
Download citation
DOI: https://doi.org/10.1007/978-3-319-95312-0_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-95311-3
Online ISBN: 978-3-319-95312-0
eBook Packages: Computer ScienceComputer Science (R0)