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Hybrid Swarms Optimization Based Image Segmentation Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Modeling of High-Dimensional Data for Applications of Image Segmentation in Image Retrieval and Recognition Tasks

Author : Dariusz Jakóbczak

Published in: Hybrid Soft Computing for Image Segmentation

Publisher: Springer International Publishing

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Abstract

Probabilistic Features Combination method (PFC), is the approach of multidimensional data modeling, extrapolation and interpolation using the set of high-dimensional feature vectors. This method is a hybridization of numerical methods and probabilistic methods with N-dimensional data interpolation for feature vectors. Each feature is treated as a random variable.

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previous chapter Hybrid Intelligent Techniques for Segmentation of Breast Thermograms
Literature
1.
Marti, U.-V., Bunke, H.: The IAM-database: an English sentence database for offline handwriting recognition. Int. J. Doc. Anal. Recognit. 5, 39–46 (2002)CrossRefMATH
2.
Djeddi, C., Souici-Meslati, L.: A texture based approach for Arabic writer identification and verification. In: International Conference on Machine and Web Intelligence, pp. 115–120 (2010)
3.
Djeddi, C., Souici-Meslati, L.: Artificial immune recognition system for Arabic writer identification. In: International Symposium on Innovation in Information and Communication Technology, pp. 159–165 (2011)
4.
Nosary, A., Heutte, L., Paquet, T.: Unsupervised writer adaption applied to handwritten text recognition. Pattern Recogn. Lett. 37(2), 385–388 (2004)CrossRefMATH
5.
Bulacu, M., Schomaker, L., Brink, A.: Text-independent writer identification and verification on off-line Arabic handwriting. In: International Conference on Document Analysis and Recognition, pp. 769–773 (2007)
6.
Chen, J., Cheng, W., Lopresti, D.: Using perturbed handwriting to support writer identification in the presence of severe data constraints. In: Document Recognition and Retrieval, pp. 1–10 (2011)
7.
Chen, J., Lopresti, D., Kavallieratou, E.: The impact of ruling lines on writer identification. In: International Conference on Frontiers in Handwriting Recognition, pp. 439–444 (2010)
8.
Galloway, M.M.: Texture analysis using gray level run lengths. Comput. Graphics Image Process. 4(2), 172–179 (1975)CrossRef
9.
Garain, U., Paquet, T.: Off-line multi-script writer identification using AR coefficients. In: International Conference on Document Analysis and Recognition, pp. 991–995 (2009)
10.
Ghiasi, G., Safabakhsh, R.: Offline text-independent writer identification using codebook and efficient code extraction methods. Image Vis. Comput. 31, 379–391 (2013)CrossRef
11.
Ozaki, M., Adachi, Y., Ishii, N.: Examination of effects of character size on accuracy of writer recognition by new local arc method. In: International Conference on Knowledge-Based Intelligent Information and Engineering Systems, pp. 1170–1175 (2006)
12.
Schlapbach, A., Bunke, H.: A writer identification and verification system using HMM based recognizers. Pattern Anal. Appl. 10, 33–43 (2007)MathSciNetCrossRef
13.
Schomaker, L., Franke, K., Bulacu, M.: Using codebooks of fragmented connected- component contours in forensic and historic writer identification. Pattern Recogn. Lett. 28(6), 719–727 (2007)CrossRef
14.
Shahabinejad, F., Rahmati, M.: A new method for writer identification and verification based on Farsi/Arabic handwritten texts, Ninth International Conference on Document Analysis and Recognition (ICDAR 2007), pp. 829–833 (2007)
15.
Siddiqi, I., Cloppet, F., Vincent, N.: Contour based features for the classification of ancient manuscripts. In: Conference of the International Graphonomics Society, pp. 226–229 (2009)
16.
Siddiqi, I., Vincent, N.: Text independent writer recognition using redundant writing patterns with contour-based orientation and curvature features. Pattern Recogn. Lett. 43(11), 3853–3865 (2010)CrossRefMATH
17.
Van, E.M., Vuurpijl, L., Franke, K., Schomaker, L.: The WANDA measurement tool for forensic document examination. J. Forensic Doc. Exam. 16, 103–118 (2005)
18.
Schlapbach, A., Bunke, H.: Off-line writer identification using Gaussian mixture models. In: International Conference on Pattern Recognition, pp. 992–995 (2006)
19.
Schlapbach, A., Bunke, H.: Using HMM based recognizers for writer identification and verification. In: 9th International Workshop on Frontiers in Handwriting Recognition, pp. 167–172 (2004)
20.
Bulacu, M., Schomaker, L.: Text-independent writer identification and verification using textural and allographic features. IEEE Trans. Pattern Anal. Mach. Intell. 29(4), 701–717 (2007)CrossRef
21.
Chapra, S.C.: Applied Numerical Methods. McGraw-Hill, Boston (2012)
22.
Collins II, G.W.: Fundamental Numerical Methods and Data Analysis. Case Western Reserve University (2003)
23.
Ralston, A., Rabinowitz, P.: A First Course in Numerical Analysis, 2nd edn. Dover Publications, New York (2001)MATH
24.
Schumaker, L.L.: Spline Functions: Basic Theory. Cambridge Mathematical Library (2007)
25.
Zhang, D., Lu, G.: Review of shape representation and description techniques. Pattern Recogn. 1(37), 1–19 (2004)CrossRef
26.
Jakobczak, D.J.: 2D Curve Modeling via the Method of Probabilistic Nodes Combination - Shape Representation, Object Modeling and Curve Interpolation-Extrapolation with the Applications. LAP Lambert Academic Publishing, Saarbrucken (2014)
Metadata
Title
Modeling of High-Dimensional Data for Applications ofImage Image Segmentation inImage Image Retrieval andRecognition Recognition Tasks
Author
Dariusz Jakóbczak
Copyright Year
2016
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_12

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