Optimal Multi-Level Thresholding Based on Maximum Tsallis Entropy via an Artificial Bee Colony Approach

Entropy - Tập 13 Số 4 - Trang 841-859
Yudong Zhang1, Lenan Wu1
1School of Information Science and Engineering, Southeast University, Nanjing 210096, China

Tóm tắt

This paper proposes a global multi-level thresholding method for image segmentation. As a criterion for this, the traditional method uses the Shannon entropy, originated from information theory, considering the gray level image histogram as a probability distribution, while we applied the Tsallis entropy as a general information theory entropy formalism. For the algorithm, we used the artificial bee colony approach since execution of an exhaustive algorithm would be too time-consuming. The experiments demonstrate that: 1) the Tsallis entropy is superior to traditional maximum entropy thresholding, maximum between class variance thresholding, and minimum cross entropy thresholding; 2) the artificial bee colony is more rapid than either genetic algorithm or particle swarm optimization. Therefore, our approach is effective and rapid.

Từ khóa


Tài liệu tham khảo

Arias, 2010, Neuro semantic thresholding using OCR software for high precision OCR applications, Image Vision Comput., 28, 571, 10.1016/j.imavis.2009.09.011

Xue, 2010, Infrared gait recognition based on wavelet transform and support vector machine, Patt. Recog., 43, 2904, 10.1016/j.patcog.2010.03.011

Anagnostopoulos, 2009, SVM-based target recognition from synthetic aperture radar images using target region outline descriptors, Nonlinear Anal.-Theor. Meth. App., 71, e2934, 10.1016/j.na.2009.07.030

Hsiao, 2006, Robust multiple objects tracking using image segmentation and trajectory estimation scheme in video frames, Image Vision Comput., 24, 1123, 10.1016/j.imavis.2006.04.002

Doelken, 2008, 1H-MRS profile in MRI positive- versus MRI negative patients with temporal lobe epilepsy, Seizure, 17, 490, 10.1016/j.seizure.2008.01.008

Wang, 2008, A novel image thresholding method based on Parzen window estimate, Patt. Recog., 41, 117, 10.1016/j.patcog.2007.03.029

Fan, 2007, A multi-level thresholding approach using a hybrid optimal estimation algorithm, Patt. Recog. Lett., 28, 662, 10.1016/j.patrec.2006.11.005

Zahara, 2005, Optimal multi-thresholding using a hybrid optimization approach, Patt. Recog. Lett., 26, 1082, 10.1016/j.patrec.2004.10.003

Nakib, 2008, Non-supervised image segmentation based on multiobjective optimization, Patt. Recog. Lett., 29, 161, 10.1016/j.patrec.2007.09.008

Cheriet, 2010, A multi-scale framework for adaptive binarization of degraded document images, Patt. Recog., 43, 2186, 10.1016/j.patcog.2009.12.024

Chung, 2009, Fast incremental algorithm for speeding up the computation of binarization, Appl. Math. Comput., 212, 396

Huang, 2009, Optimal multi-level thresholding using a two-stage Otsu optimization approach, Patt. Recog. Lett., 30, 275, 10.1016/j.patrec.2008.10.003

Wang, 2010, Fast three-dimensional Otsu thresholding with shuffled frog-leaping algorithm, Patt. Recog. Lett., 31, 1809, 10.1016/j.patrec.2010.06.002

Horng, 2010, A multilevel image thresholding using the honey bee mating optimization, Appl. Math. Comput., 215, 3302

Yin, 2007, Multilevel minimum cross entropy threshold selection based on particle swarm optimization, Appl. Math. Comput., 184, 503

Hamza, 2006, Nonextensive information-theoretic measure for image edge detection, J. Electron. Imag., 15, 1

Parvan, 2010, Critique of multinomial coefficients method for evaluating Tsallis and Rényi entropies, Physica A, 389, 5645, 10.1016/j.physa.2010.08.040

Nakib, 2010, Image thresholding based on Pareto multiobjective optimization, Eng. Appl. Artif. Intell., 23, 313, 10.1016/j.engappai.2009.09.002

Maitra, 2008, A hybrid cooperative-comprehensive learning based PSO algorithm for image segmentation using multilevel thresholding, Expert Syst. Appl., 34, 1341, 10.1016/j.eswa.2007.01.002

Karaboga, 2004, Designing digital IIR filters using ant colony optimisation algorithm, Eng. Appl. Artif. Intell., 17, 301, 10.1016/j.engappai.2004.02.009

Karaboga, 2008, On the performance of artificial bee colony (ABC) algorithm, Appl. Soft Comput., 8, 687, 10.1016/j.asoc.2007.05.007

Xu, C., Duan, H., and Liu, F. (2011). Chaotic artificial bee colony approach to Uninhabited Combat Air Vehicle (UCAV) path planning. Aerosp. Sci. Technol., in press.

Guida, 2010, Shannon entropy for local and global description of mixing by Lagrangian particle tracking, Chem. Eng. Sci., 65, 2865, 10.1016/j.ces.2009.12.041

Campos, 2010, Real and spurious contributions for the Shannon, Rényi and Tsallis entropies, Physica A, 389, 3761, 10.1016/j.physa.2010.05.029

Esquef, 2004, Image thresholding using Tsallis entropy, Patt. Recog. Lett., 25, 1059, 10.1016/j.patrec.2004.03.003

Kang, 2009, Structural inverse analysis by hybrid simplex artificial bee colony algorithms, Comput. Struct., 87, 861, 10.1016/j.compstruc.2009.03.001

Baykasoglu, 2010, Bees algorithm for generalized assignment problem, Appl. Math. Comput., 215, 3782

Zarezadeh, 2010, Results on residual Rényi entropy of order statistics and record values, Inform. Sci., 180, 4195, 10.1016/j.ins.2010.06.019