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2016 | OriginalPaper | Chapter

Improved Human Skin Segmentation Using Fuzzy Fusion Based on Optimized Thresholds by Genetic Algorithms

Authors : Anderson Santos, Jônatas Paiva, Claudio Toledo, Helio Pedrini

Published in: Hybrid Soft Computing for Image Segmentation

Publisher: Springer International Publishing

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Abstract

Human skin segmentation has several applications in computer vision beyond its main purpose of distinguishing between skin and nonskin regions. Despite the large number of methods available in the literature, accurate skin segmentation is still a challenging task. Many methods rely only on color information, which does not completely discriminate the image regions due to variations in lighting conditions and ambiguity between skin and background color. This chapter extends upon a self-contained method for skin segmentation that outlines regions from which the overall skin color can be estimated and such that the color model is adjusted to a particular image. This process is based on thresholds that were empirically defined in a first approach. The proposed method has three main contributions over the previous one. First, genetic algorithm (GA) is applied to search for better thresholds that will be used to extract appropriate seeds from the general probability and texture maps. Next, the GA is also applied to define thresholds for edge detectors aiming to improve edge connections. Finally, a fuzzy method for fusion is included where its parameters are optimized by GA during a learning phase. The improvements added to the skin segmentation method are evaluated on a set of hand gesture images. A statistical analysis is conducted over the computational results achieved by each evaluated method, indicating a superior performance of our novel skin segmentation method.

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Hu, X., Peng, S., Yan, J., Zhang, N.: Fast face detection based on skin color segmentation using single Chrominance Cr. In: 7th International Congress on Image and Signal Processing, pp. 687–692. IEEE (2014)

Ji, S., Lu, X., Xu, Q.: A fast face detection method combining skin color feature and adaboost. In: International Conference on Multisensor Fusion and Information Integration for Intelligent Systems, pp. 1–5. IEEE (2014)

Palacios, J.M., Sagüés, C., Montijano, E., Llorente, S.: Human-computer interaction based on hand gestures using RGB-D sensors. Sensors 13(9), 11842–11860 (2013)CrossRef

Wachs, J.P., Kölsch, M., Stern, H., Edan, Y.: Vision-based hand-gesture applications. Commun. ACM 54(2), 60–71 (2011). FebCrossRef

Jones, M.J., Rehg, J.M.: Statistical color models with application to skin detection. Int. J. Comput. Vis. 46(1), 81–96 (2002)CrossRefMATH

Vo, D.M., Jiang, L., Zell, A.: Real time person detection and tracking by mobile robots using RGB-D images. In: IEEE International Conference on Robotics and Biomimetics, pp. 689–694. IEEE (2014)

Jeong, C.-Y., Kim, J.-S., Hong, K.-S.: Appearance-based nude image detection. In: 17th International Conference on Pattern Recognition, vol. 4, pp. 467–470. IEEE (2004)

Platzer, C., Stuetz, M., Lindorfer, M.: Skin sheriff: a machine learning solution for detecting explicit images. In: 2nd International Workshop on Security and Forensics in Communication Systems, pp. 45–56. ACM, New York (2014)

Acton, S.T., Rossi, A.: Matching and retrieval of tattoo images: active contour CBIR and glocal image features. In: IEEE Southwest Symposium on Image Analysis and Interpretation, pp. 21–24. IEEE (2008)

10.

Choraś, R.S.: CBIR System for detecting and blocking adult images. In: 9th WSEAS International Conference on Signal Processing, pp. 52–57. World Scientific and Engineering Academy and Society, Stevens Point (2010)

11.

Manresa-Yee, C., Varona, J., Mas, R., Perales, F.J.: Hand tracking and gesture recognition for human-computer interaction. Progress In Computer Vision And Image, Analysis, pp. 401–412 (2010)

12.

Ren, Z., Meng, J., Yuan, J.: Depth camera based hand gesture recognition and its applications in human-computer-interaction. In: 8th International Conference on Information, Communications and Signal Processing, pp. 1–5. IEEE (2011)

13.

Santos, A., Pedrini, H.: A Self-adaptation method for human skin segmentation based on seed growing. In: 10th International Conference on Computer Vision Theory and Applications, pp. 455–462. Berlin (2015)

14.

Kakumanu, P., Makrogiannis, S., Bourbakis, N.: A survey of skin-color modeling and detection methods. Pattern Recognit. 40(3), 1106–1122 (2007)CrossRefMATH

15.

Kawulok, M., Nalepa, J., Kawulok, J.: Skin detection and segmentation in color images. Advances in Low-Level Color Image Processing, pp. 329–366. Springer, Berlin (2014)

16.

Phung, S.L., Bouzerdoum, A., Chai, D.: Skin segmentation using color pixel classification: analysis and comparison. IEEE Trans. Pattern Anal. Mach. Intell. 27(1), 148–154 (2005)CrossRef

17.

Zarit, B.D., Super, B.J., Quek, F.K.: Comparison of five color models in skin pixel classification. In: International Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems, pp. 58–63 (1999)

18.

Sobottka, K., Pitas, I.: Face localization and facial feature extraction based on shape and color information. In: International Conference on Image Processing, vol. 3, pp. 483–486. IEEE (1996)

19.

Cheddad, A., Condell, J., Curran, K., Mc Kevitt, P.: A skin tone detection algorithm for an adaptive approach to steganography. Signal Process. 89(12), 2465–2478 (2009)

20.

Hsu, R.-L., Abdel-Mottaleb, M., Jain, A.K.: Face detection in color images. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 696–706 (2002)CrossRef

21.

Soriano, M., Martinkauppi, B., Huovinen, S., Laaksonen, M.: Skin detection in video under changing illumination conditions. In 15th International Conference on Pattern Recognition, vol. 1, pp. 839–842. IEEE (2000)

22.

Kovac, J., Peer, P., Solina, F.: Human skin color clustering for face detection. In: International Conference on Computer as a Tool. vol. 2, pp. 144–148. IEEE (2003)

23.

Phung, S.L., Chai, D., Bouzerdoum, A.: Adaptive skin segmentation in color images. In: International Conference on Multimedia and Expo, vol. 3, pp. 111–173 (2003)

24.

Fritsch, J., Lang, S., Kleinehagenbrock, M., Fink, G.A., Sagerer, G.: Improving adaptive skin color segmentation by incorporating results from face detection. In: 11th IEEE International Workshop on Robot and Human Interactive Communication, pp. 337–343 (2002)

25.

Taylor, M.J., Morris, T.: Adaptive skin segmentation via feature-based face detection. In: SPIE Photonics Europe, p. 91390P. International Society for Optics and Photonics (2014)

26.

Kawulok, M.: Energy-based blob analysis for improving precision of skin segmentation. Multimed. Tools Appl. 49(3), 463–481 (2010)CrossRef

27.

Kawulok, M.: Fast propagation-based skin regions segmentation in color images. In: 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, pp. 1–7 (2013)

28.

Ruiz-del Solar, J., Verschae, R.: Skin Detection using Neighborhood Information. In: Sixth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 463–468. IEEE (2004)

29.

Dijkstra, E.W.: A note on two problems in connection with graphs. Numer. Math. 1, 269–271 (1959)MathSciNetCrossRefMATH

30.

Wang, X., Zhang, X., Yao, J.: Skin color detection under complex background. In: International Conference on Mechatronic Science, Electric Engineering and Computer, pp. 1985–1988 (2011)

31.

Gonzalez, R., Woods, R., Eddins, S.: Digital Image Processing using MATLAB. Gatesmark Publishing, Knoxville (2009)

32.

Schwartz, W.R., Pedrini, H.: Color textured image segmentation based on spatial dependence using 3D co-occurrence matrices and Markov random fields. 15th International Conference in Central Europe on Computer Graphics. Visualization and Computer Vision, pp. 81–87. Czech Republic (2007)

33.

Ng, P., Chi-Man, P.: Skin color segmentation by texture feature extraction and K-means clustering. In: Third International Conference on Computational Intelligence, Communication Systems and Networks, pp. 213–218. IEEE (2011)

34.

Jiang, Z., Yao, M., Jiang, W.: Skin detection using color, texture and space information. Fourth Int. Conf. Fuzzy Syst. Knowl. Discov. 3, 366–370 (2007)CrossRef

35.

Gupta, V., Chan, C.C., Sian, P.T.: A differential evolution approach to PET image de-noising. In: 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4173–4176 (2007)

36.

Thavavel, V., Basha, J.J., Krishna, M., Murugesan, R.: Heuristic wavelet approach for low-dose EPR tomographic reconstruction: an applicability analysis with phantom and in vivo imaging. Expert Syst. Appl. 39(5), 5717–5726 (2012)CrossRef

37.

Mukhopadhyay, S., Mandal, J.: Wavelet based denoising of medical images using sub-band adaptive thresholding through genetic algorithm. Procedia Technol. 10, 680–689 (2013) (First International Conference on Computational Intelligence: Modeling Techniques and Applications)

38.

Xie, F., Bovik, A.C.: Automatic segmentation of dermoscopy images using self-generating neural networks seeded by genetic algorithm. Pattern Recognit. 46(3), 1012–1019 (2013)CrossRef

39.

Razmjooy, N., Mousavi, B.S., Soleymani, F.: A hybrid neural network imperialist competitive algorithm for skin color segmentation. Math. Comput. Model. 57(3–4), 848–856 (2013)CrossRef

40.

Chahir, Y., Elmoataz, A.: Skin-color detection using fuzzy clustering. Int. Symp. Commun. Control Signal Process. 3(1), 1–4 (2006)

41.

Hmida, M.B., Jemaa, Y.B.: Fuzzy classification, image segmentation and shape analysis for human face detection. In: 13th IEEE International Conference on Electronics, Circuits and Systems, pp. 640–643. IEEE (2006)

42.

Kim, M.H., Park, J.B., Joo, Y.H.: New fuzzy skin model for face detection. Advances in Artificial Intelligence, pp. 557–566. Springer, Berlin (2005)

43.

Soille, P.: Morphological Image Analysis: Principles and Applications. Springer Science & Business Media, Berlin (2013)

44.

Laws, K.I.: Rapid texture identification. In: 24th Annual Technical Symposium, International Society for Optics and Photonics, pp. 376–381 (1980)

45.

Santos, A., Pedrini, H.: Human skin segmentation improved by texture energy under superpixels. In: Pardo, A., Kittler, J. (eds.) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Volume 9423 of Lecture Notes in Computer Science, pp. 35–42. Springer International Publishing, Berlin (2015)

46.

Pratt, W.K.: Digital Image Processing. Wiley-Interscience, New York (2001)

47.

Kitchen, L., Rosenfeld, A.: Edge evaluation using local edge coherence. IEEE Trans. Syst. Man Cybern. 11(9), 597–605 (1981)CrossRef

48.

Zhu, Q.: Efficient evaluations of edge connectivity and width uniformity. Image Vis. Comput. 14(1), 21–34 (1996) (Image and Vision Computing Journal on Vision-Based Aids for the Disabled)

49.

Tao, C., Xiankun, S., Hua, H., Xiaoming, Y.: Image edge detection based on ACO-PSO algorithm. Int. J. Adv. Comput. Sci. Appl. 6(7), 47–54 (2015)

50.

Soria-Frisch, A.: Soft Data Fusion for Computer Vision. Fraunhofer-IRB-Verlag (2004)

51.

Murofushi, T., Sugeno, M.: Fuzzy measures and fuzzy integrals. In: Grabisch, M., Murofushi, T., Sugeno, M. (eds.) Fuzzy Measures and Integrals - Theory and Applications, pp. 3–41. Physica Verlag, Heidelberg (2000)

52.

Murofushi, T., Sugeno, M.: An interpretation of fuzzy measures and the Choquet integral as an integral with respect to a fuzzy measure. Fuzzy Sets Syst. 29(2), 201–227 (1989)MathSciNetCrossRefMATH

53.

Soria-Frisch, A., Verschae, R., Olano, A.: Fuzzy fusion for skin detection. Fuzzy Sets Syst. 158(3), 325–336 (2007)MathSciNetCrossRef

54.

Tahani, H., Keller, J.M.: Information fusion in computer vision using the fuzzy integral. IEEE Trans. Syst. Man Cybern. 20(3), 733–741 (1990)CrossRef

55.

Global Optimization Toolbox. http://www.mathworks.com/products/global-optimization/ (2016). Accessed 24 Feb 2016

56.

Baker, J.E.: Reducing bias and inefficiency in the selection algorithm. In: Second International Conference on Genetic Algorithms on Genetic Algorithms and Their Application, pp. 14–21. L. Erlbaum Associates Inc., Hillsdale (1987)

57.

Kawulok, M., Kawulok, J., Nalepa, J.: Spatial-based skin detection using discriminative skin-presence features. Pattern Recognit. Lett. 41, 3–13 (2014)CrossRef

58.

Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures. CRC Press, Boca Raton (2003)

59.

Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)MathSciNetMATH

Title: Improved Human Skin Segmentation Using Fuzzy Fusion Based on Optimized Thresholds by Genetic Algorithms
Authors: Anderson Santos
Jônatas Paiva
Claudio Toledo
Helio Pedrini
Publisher: Springer International Publishing
Book: Hybrid Soft Computing for Image Segmentation
Print ISBN: 978-3-319-47222-5

Electronic ISBN: 978-3-319-47223-2

Copyright Year: 2016
DOI: https://doi.org/10.1007/978-3-319-47223-2_8

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