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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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 |