DISCRETIZATION OF INTEGRATED MOMENT INVARIANTS FOR WRITER IDENTIFICATION

Conservative regular moments have been proven to exhibit some shortcomings in the original formulations of moment functions in terms of scaling factor. Hence, an incorporated scaling factor of geometric functions into United Moment Invariant function is proposed for mining the feature of uncons...

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Main Authors:	Muda, A. K., Shamsuddin, S. M., Darus, M.
Format:	Conference or Workshop Item
Published:	2008
Subjects:	TA Engineering (General). Civil engineering (General)
Online Access:	http://eprints.utem.edu.my/34/ http://eprints.utem.edu.my/34/1/605-081_ACST2008.pdf

id	utem-34
recordtype	eprints
spelling	utem-342015-05-28T02:16:32Z DISCRETIZATION OF INTEGRATED MOMENT INVARIANTS FOR WRITER IDENTIFICATION Muda, A. K. Shamsuddin, S. M. Darus, M. TA Engineering (General). Civil engineering (General) Conservative regular moments have been proven to exhibit some shortcomings in the original formulations of moment functions in terms of scaling factor. Hence, an incorporated scaling factor of geometric functions into United Moment Invariant function is proposed for mining the feature of unconstrained words. Subsequently, the discrete proposed features undertake discretization procedure prior to classification for better feature representation and splendid classification accuracy. Collectively, discrete values are finite intervals in a continuous spectrum of values and well known to play important roles in data mining and knowledge discovery. Many induction algorithms found in the literature requires that training data contains only discrete features and some works better on discretized data; in particular rule based approaches like rough sets. Hence, in this study, an integrated scaling formulation of Aspect Scaling Invariant is presented in Writer Identification to hunt for the individuality perseverance. Successive exploration is executed to investigate for the suitability of discretization techniques in probing the issues of writer authorship. Mathematical proving and results of computer simulations are embraced to attest the feasibility of the proposed technique in Writer Identification. The results disclose that the proposed discretized invariants reveal 99% accuracy of classification by using 3520 training data and 880 testing data. 2008 Conference or Workshop Item PeerReviewed application/pdf http://eprints.utem.edu.my/34/1/605-081_ACST2008.pdf Muda, A. K. and Shamsuddin, S. M. and Darus, M. (2008) DISCRETIZATION OF INTEGRATED MOMENT INVARIANTS FOR WRITER IDENTIFICATION. In: International Conference on Advances in Computer Science and Technology ACST2008, 2 - 4 April, 2008, Langkawi, Malaysia. http://eprints.utem.edu.my/34/
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Teknikal Malaysia Melaka
building	UTeM Institutional Repository
collection	Online Access
topic	TA Engineering (General). Civil engineering (General)
spellingShingle	TA Engineering (General). Civil engineering (General) Muda, A. K. Shamsuddin, S. M. Darus, M. DISCRETIZATION OF INTEGRATED MOMENT INVARIANTS FOR WRITER IDENTIFICATION
description	Conservative regular moments have been proven to exhibit some shortcomings in the original formulations of moment functions in terms of scaling factor. Hence, an incorporated scaling factor of geometric functions into United Moment Invariant function is proposed for mining the feature of unconstrained words. Subsequently, the discrete proposed features undertake discretization procedure prior to classification for better feature representation and splendid classification accuracy. Collectively, discrete values are finite intervals in a continuous spectrum of values and well known to play important roles in data mining and knowledge discovery. Many induction algorithms found in the literature requires that training data contains only discrete features and some works better on discretized data; in particular rule based approaches like rough sets. Hence, in this study, an integrated scaling formulation of Aspect Scaling Invariant is presented in Writer Identification to hunt for the individuality perseverance. Successive exploration is executed to investigate for the suitability of discretization techniques in probing the issues of writer authorship. Mathematical proving and results of computer simulations are embraced to attest the feasibility of the proposed technique in Writer Identification. The results disclose that the proposed discretized invariants reveal 99% accuracy of classification by using 3520 training data and 880 testing data.
format	Conference or Workshop Item
author	Muda, A. K. Shamsuddin, S. M. Darus, M.
author_facet	Muda, A. K. Shamsuddin, S. M. Darus, M.
author_sort	Muda, A. K.
title	DISCRETIZATION OF INTEGRATED MOMENT INVARIANTS FOR WRITER IDENTIFICATION
title_short	DISCRETIZATION OF INTEGRATED MOMENT INVARIANTS FOR WRITER IDENTIFICATION
title_full	DISCRETIZATION OF INTEGRATED MOMENT INVARIANTS FOR WRITER IDENTIFICATION
title_fullStr	DISCRETIZATION OF INTEGRATED MOMENT INVARIANTS FOR WRITER IDENTIFICATION
title_full_unstemmed	DISCRETIZATION OF INTEGRATED MOMENT INVARIANTS FOR WRITER IDENTIFICATION
title_sort	discretization of integrated moment invariants for writer identification
publishDate	2008
url	http://eprints.utem.edu.my/34/ http://eprints.utem.edu.my/34/1/605-081_ACST2008.pdf
first_indexed	2018-09-05T09:47:26Z
last_indexed	2018-09-05T09:47:26Z
_version_	1610760424037285888

DISCRETIZATION OF INTEGRATED MOMENT INVARIANTS FOR WRITER IDENTIFICATION

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