Abstract
Thresholding-based image segmentation algorithms are usually developed for a specific set of images because the objective of these algorithms is strongly related to their applications. The binarization of the image is generally preferred over multi-segmentation, mainly because it’s simple and easy to implement. However, in this paper we demonstrate that a scene separation with three threshold levels can be more effective and closer to a manually performed segmentation. Also, we show that similar results can be achieved through a firefly-based meta-heuristic. Finally, we suggest a similarity measure that can be used for the comparison between the distances of the automatic and manual segmentation.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
we define the composed distribution, also called direct product of \(P=(p_{1},\ldots,p_{n})\) and \(Q=(q_{1},\ldots,q_{m})\), as \(P\ast Q=\{p_{i}q_{j}\}_{i,j}\), with \(1\leq i\leq n\) and \(1\leq j\leq m\)
References
Pun T (1981) Entropic thresholding: a new approach. Comput Graphics Image Process 16:210–239
Kapur JN, Sahoo PK, Wong AKC (1985) A new method for gray-level picture thresholding using the entropy of the histogram. Comput. Graphics Image Process 29:273–285
Abutaleb AS (1989) A new method for gray-level picture thresholding using the entropy of the histogram. Comput Graphics Image Process 47:22–32
Li CH, Lee CK (1993) Minimum cross entropy thresholding. Pattern Recognit 26:617–625
Pal NR (1996) On minimum cross entropy thresholding. Pattern Recognit 26:575–580
Sahoo P, Soltani S, Wong A, Chen Y (1988) A survay of thresholding techniques. Comput Vis Gr Image Process 41(1):233–260
Chang C-I, Du Y, Wang J, Guo S-M, Thouin P (2006, Dec.) Survey and comparative analysis of entropy and relative entropy thresholding techniques. IEEE Proc, Vis, Image Signal Process 153(6):837–850
Albuquerque M, Esquef I, Mello A (2004) Image thresholding using tsallis entropy. J Stat Phys 25:1059–1065
Tsallis C (1999, March) Nonextensive statistics: theoretical, experimental and computational evidences and connections. Braz J Phys 29(1):1–35
Yin PY (2007) Multilevel minimum cross entropy threshold selection based on particle swarm optimization. Appl Math Comput 184:503–513
Horng MH, Liou RJ (2011) Multilevel minimum cross entropy threshold selection based on firefly algorithm. Expert Syst Appl 38:14805–14811
Martin D, Fowlkes C, Tal D, Malik J (2001, July) A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proc. 8th Int’l Conf. Computer Vision, vol 2, pp 416–423
Yang XS (2009) Firefly algorithms for multimodal optimization. Stochastic algorithms: fundation and applications, SAGA 2009. Lecture Notes Computer Science 5792:169–178
Erdmann H, Lopes LA, Wachs-Lopes G, Ribeiro MP, Rodrigues PS (2013) A study of firefly meta-heuristic for multithresholding image segmentation. In: VIpImage: Thematic Conference on Computational Vision and Medical Image Processing, Ilha da Madeira, Portugal, October, 14 to 16 2013, pp 211–217
Lukasik S, Zak S (2009) Firefly algorithm for continuous constrained optimization tasks. In: 1st International Conference on Computational Collective Intelligence, Semantic Web, 5-7 October 2009.
Dorigo M (1992) Optimization, learning, and natural algorithms. Ph. D. Thesis, Dipartimento di Elettronica e Informazione, Politecnico di Milano, Italy
Glover F (1989) Tabu search. PART I, ORSA J Comput 1:190–206
Kennedy J, Goldberg RC (1997) Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, vol IV, pp 1942–1948
Goldberg DE (1997) Genetic algorithms in search, optimization, and machine learning. Addison Wesley, Reading
Hassanzadeh T, Vojodi H, Eftekhari AM (2011) An image segmentation approach based on maximum variance intra-cluster method and firefly algorithm. In: Seventh International Conference on Natural Computation, IEEE, Ed., Shanghai, China, pp 1844–1848
Shannon C, Weaver W (1948) The mathematical theory of communication. University of Illinois Press, Urbana
Tavares AHMP (2003) Aspectos matemáticos da entropia. Master Thesis, Universidade de Aveiro
Giraldi G, Rodrigues P (2009) Improving the non-extensive tsallis non-extensive medical image segmentation based on tsallis entropy. Pattern Analysis and Application, vol. Submitted
Rodrigues P, Giraldi G (2009) Computing the q-index for tsallis non-extensive image segmentation. In XXII Brazilian Symposium on Computer Graphics and Image Processing (Sibgrapi 2009), SBC, Ed., vol. To Appear
Sezgin M, Sankur B (2004, Jan) Survay ove image thresholding techniques and quantitative performance evaluation. J Eletr Imaging 13(1):146–165
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Erdmann, H., Wachs-Lopes, G., Gallão, C., Ribeiro, M., Rodrigues, P. (2015). A Study of a Firefly Meta-Heuristics for Multithreshold Image Segmentation. In: Tavares, J., Natal Jorge, R. (eds) Developments in Medical Image Processing and Computational Vision. Lecture Notes in Computational Vision and Biomechanics, vol 19. Springer, Cham. https://doi.org/10.1007/978-3-319-13407-9_17
Download citation
DOI: https://doi.org/10.1007/978-3-319-13407-9_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13406-2
Online ISBN: 978-3-319-13407-9
eBook Packages: EngineeringEngineering (R0)