NDLI: Data normalization for triangle features by adapting triangle nature for better classification

Content Provider	IEEE Xplore Digital Library
Author	Azmi, M.S. Arbain, N.A. Muda, A.K. Abas, Z.A. Muslim, Z.
Copyright Year	2015
Description	Author affiliation: Fac. of Inf. Commun. & Technol., Univ. Teknikal Malaysia Melaka, Melaka, Malaysia (Azmi, M.S.; Arbain, N.A.; Muda, A.K.; Abas, Z.A.; Muslim, Z.)
Abstract	Geometry features especially triangle has been widely used in face, fingerprint, vehicle detection and digit recognition. Features from the triangle are used to generate useful features for classification processed. Recently, triangle features used in digit recognition has adopted angle as part of features. This has influenced accuracy due to big gap between angle values and other feature values such as ratio and gradient of sides. To overcome this issue, data normalization can be used to address the issue. Experiments have been made using existing normalization techniques such as Z-score, Minimax and libSVM scale function. Experiments have been conducted using Z-Score and libSVM scale function, but results of classification are worst compared to triangle features without normalization. Thus, the results of classification can be improved by proposed a new technique of normalization based on nature of triangle geometry. In this paper, we have proposed a new normalization technique by adopting the nature of triangle geometry. Datasets HODA, MNIST, IFHCDB and BANGLA digit have been chosen to extract triangle features. Then, we will apply normalization on the extracted features before classify them by using Support Vector Machine. The results shows normalization by adapting the nature of triangle geometry gives better result compared to other techniques. The proposed normalization technique only applies to Cartesian Plane Zone that contributes 45 features. The benchmarking for other researchers should refer to our 25 zones that give 225 features of triangle geometry.
Starting Page	1
Ending Page	6
File Size	1431178
Page Count	6
File Format	PDF
ISBN	9781479974429
e-ISBN	9781479974313
DOI	10.1109/AEECT.2015.7360572
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-11-03
Publisher Place	Jordan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Geometry Support vector machines Electrical engineering Computers Conferences Feature Extraction Feature extraction Triangle features Classification algorithms Feature Normalization Feature Scaling Triangle Geometry
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Mr. Siddhartha Mukherjee siddhartha@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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