Shape-based recognition using combined Jaccard and Mahalanobis measurement / Noor Aznimah Abdul Aziz

Numerous well-established shape recognition approaches for handling variance of image transformations and strokes variations in free-hand digital sketching environment but none has satisfactorily deal with object features yet. Complexity in the computation measure, expensive in computation time, req...

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Main Author:	Abdul Aziz, Noor Aznimah
Format:	Thesis
Language:	English
Published:	2013
Subjects:	Instruments and machines
Online Access:	https://ir.uitm.edu.my/id/eprint/12214/2/12214.pdf
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id	my-uitm-ir.12214
record_format	uketd_dc
spelling	my-uitm-ir.122142023-08-22T02:14:39Z Shape-based recognition using combined Jaccard and Mahalanobis measurement / Noor Aznimah Abdul Aziz 2013 Abdul Aziz, Noor Aznimah Instruments and machines Numerous well-established shape recognition approaches for handling variance of image transformations and strokes variations in free-hand digital sketching environment but none has satisfactorily deal with object features yet. Complexity in the computation measure, expensive in computation time, requires large training data set and extensive training is not practical, especially in sketching recognition. In contrast, methods for similarity measurement such as Jaccard distance, Mahalanobis distance and others are commonly used in recognition tasks offer a simple computation, not require a large training data set and can handle variances of image transformations and strokes variations. Therefore, we proposed a shape recognition algorithm using similarity measurement combining Jaccard and Mahalanobis distance is used to measure the similarity between geometry shape sketches. Two major pre processing procedures involved feature extraction and edges perfection were performed for shape normalization and beautification. The new combined algorithm also implements edges separation and masking technique to improve similarity measurement and reduce the amount of testing data set used. Results show that the combination of Jaccard and Mahalanobis distance increase similarity percentages from 18% to 66%, thus accrued an improvement of 48% differences. Having this difference, the two major contributions made in this study are first a combined algorithm and a new technique of separating edges in Jaccard and the use of extreme vertices in Mahalanobis distance. This proposed combined algorithms and techniques can achieve high performance in shape similarity measurement recognition and also the masking technique in Mahalanobis distance measurement can reduce the amount of data analysis. 2013 Thesis https://ir.uitm.edu.my/id/eprint/12214/ https://ir.uitm.edu.my/id/eprint/12214/2/12214.pdf text en public mphil masters Universiti Teknologi MARA (UiTM) Faculty of Computer and Mathematical Sciences Salleh, Siti Salwa
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Salleh, Siti Salwa
topic	Instruments and machines
spellingShingle	Instruments and machines Abdul Aziz, Noor Aznimah Shape-based recognition using combined Jaccard and Mahalanobis measurement / Noor Aznimah Abdul Aziz
description	Numerous well-established shape recognition approaches for handling variance of image transformations and strokes variations in free-hand digital sketching environment but none has satisfactorily deal with object features yet. Complexity in the computation measure, expensive in computation time, requires large training data set and extensive training is not practical, especially in sketching recognition. In contrast, methods for similarity measurement such as Jaccard distance, Mahalanobis distance and others are commonly used in recognition tasks offer a simple computation, not require a large training data set and can handle variances of image transformations and strokes variations. Therefore, we proposed a shape recognition algorithm using similarity measurement combining Jaccard and Mahalanobis distance is used to measure the similarity between geometry shape sketches. Two major pre processing procedures involved feature extraction and edges perfection were performed for shape normalization and beautification. The new combined algorithm also implements edges separation and masking technique to improve similarity measurement and reduce the amount of testing data set used. Results show that the combination of Jaccard and Mahalanobis distance increase similarity percentages from 18% to 66%, thus accrued an improvement of 48% differences. Having this difference, the two major contributions made in this study are first a combined algorithm and a new technique of separating edges in Jaccard and the use of extreme vertices in Mahalanobis distance. This proposed combined algorithms and techniques can achieve high performance in shape similarity measurement recognition and also the masking technique in Mahalanobis distance measurement can reduce the amount of data analysis.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Abdul Aziz, Noor Aznimah
author_facet	Abdul Aziz, Noor Aznimah
author_sort	Abdul Aziz, Noor Aznimah
title	Shape-based recognition using combined Jaccard and Mahalanobis measurement / Noor Aznimah Abdul Aziz
title_short	Shape-based recognition using combined Jaccard and Mahalanobis measurement / Noor Aznimah Abdul Aziz
title_full	Shape-based recognition using combined Jaccard and Mahalanobis measurement / Noor Aznimah Abdul Aziz
title_fullStr	Shape-based recognition using combined Jaccard and Mahalanobis measurement / Noor Aznimah Abdul Aziz
title_full_unstemmed	Shape-based recognition using combined Jaccard and Mahalanobis measurement / Noor Aznimah Abdul Aziz
title_sort	shape-based recognition using combined jaccard and mahalanobis measurement / noor aznimah abdul aziz
granting_institution	Universiti Teknologi MARA (UiTM)
granting_department	Faculty of Computer and Mathematical Sciences
publishDate	2013
url	https://ir.uitm.edu.my/id/eprint/12214/2/12214.pdf
_version_	1783733167827451904

Shape-based recognition using combined Jaccard and Mahalanobis measurement / Noor Aznimah Abdul Aziz

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