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Iban Plaited Mat Motif Classification using Adaptive Smoothing

Iban plaited mats hold a significant cultural importance amongst Borneo’s indigenous communities. These mats are intricately crafted with culturally significant patterns, incorporating a mix of diagonal, symmetrical, geometric, and non-geometric designs. Classifying these motifs poses a challenge du...

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Main Author: Silvia, Joseph
Format: Thesis
Language:English
Published: UNIMAS 2024
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://ir.unimas.my/id/eprint/45964/1/Iban%20Plaited%20Mat%20Motif%20Classification%20Using%20Adaptive%20Smoothing_PhD%20THESIS%20FINALREVIEW%20SilviaJoseph_sign.pdf
http://ir.unimas.my/id/eprint/45964/
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spelling my.unimas.ir.459642024-09-09T07:29:38Z http://ir.unimas.my/id/eprint/45964/ Iban Plaited Mat Motif Classification using Adaptive Smoothing Silvia, Joseph QA75 Electronic computers. Computer science Iban plaited mats hold a significant cultural importance amongst Borneo’s indigenous communities. These mats are intricately crafted with culturally significant patterns, incorporating a mix of diagonal, symmetrical, geometric, and non-geometric designs. Classifying these motifs poses a challenge due to their complex stylization and incorporation of multiple smaller patterns surrounding the main motif. This work introduces a new image dataset comprising of cleaned, cropped, and perspective corrected Iban plaited mat motif images. First, the accuracy of the Scale Invariant Feature Transform (SIFT) combined with the Random Sample Consensus (RANSAC) algorithm was assessed on the dataset. The optimal peak threshold value for SIFT is reported to be 2.0e-2, and it achieved 100.0% matching accuracy for scale and rotation set. Next, additional local feature descriptors, combined with RANSAC, were evaluated on an expanded dataset. BRISK+RANSAC emerged as the top performer, achieving an average matching accuracy of 85.4%. BRISK+RANSAC encountered challenges in handling unresolved classes in sets with blur, illumination, and viewpoint changes. This is attributed to the spurious classification of smaller motifs affecting the label assignment. To address this issue, an improved classification method with adaptive smoothing is proposed. This method utilizes dynamic thresholds for edge detection and uses morphological operations to enhance the smoothed edges. The proposed method attained 100.0% accuracy for several sets and obtained an average increase of 10.2% accuracy over the baseline method. UNIMAS 2024-09-05 Thesis NonPeerReviewed text en http://ir.unimas.my/id/eprint/45964/1/Iban%20Plaited%20Mat%20Motif%20Classification%20Using%20Adaptive%20Smoothing_PhD%20THESIS%20FINALREVIEW%20SilviaJoseph_sign.pdf Silvia, Joseph (2024) Iban Plaited Mat Motif Classification using Adaptive Smoothing. PhD thesis, UNIMAS.
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Silvia, Joseph
Iban Plaited Mat Motif Classification using Adaptive Smoothing
description Iban plaited mats hold a significant cultural importance amongst Borneo’s indigenous communities. These mats are intricately crafted with culturally significant patterns, incorporating a mix of diagonal, symmetrical, geometric, and non-geometric designs. Classifying these motifs poses a challenge due to their complex stylization and incorporation of multiple smaller patterns surrounding the main motif. This work introduces a new image dataset comprising of cleaned, cropped, and perspective corrected Iban plaited mat motif images. First, the accuracy of the Scale Invariant Feature Transform (SIFT) combined with the Random Sample Consensus (RANSAC) algorithm was assessed on the dataset. The optimal peak threshold value for SIFT is reported to be 2.0e-2, and it achieved 100.0% matching accuracy for scale and rotation set. Next, additional local feature descriptors, combined with RANSAC, were evaluated on an expanded dataset. BRISK+RANSAC emerged as the top performer, achieving an average matching accuracy of 85.4%. BRISK+RANSAC encountered challenges in handling unresolved classes in sets with blur, illumination, and viewpoint changes. This is attributed to the spurious classification of smaller motifs affecting the label assignment. To address this issue, an improved classification method with adaptive smoothing is proposed. This method utilizes dynamic thresholds for edge detection and uses morphological operations to enhance the smoothed edges. The proposed method attained 100.0% accuracy for several sets and obtained an average increase of 10.2% accuracy over the baseline method.
format Thesis
author Silvia, Joseph
author_facet Silvia, Joseph
author_sort Silvia, Joseph
title Iban Plaited Mat Motif Classification using Adaptive Smoothing
title_short Iban Plaited Mat Motif Classification using Adaptive Smoothing
title_full Iban Plaited Mat Motif Classification using Adaptive Smoothing
title_fullStr Iban Plaited Mat Motif Classification using Adaptive Smoothing
title_full_unstemmed Iban Plaited Mat Motif Classification using Adaptive Smoothing
title_sort iban plaited mat motif classification using adaptive smoothing
publisher UNIMAS
publishDate 2024
url http://ir.unimas.my/id/eprint/45964/1/Iban%20Plaited%20Mat%20Motif%20Classification%20Using%20Adaptive%20Smoothing_PhD%20THESIS%20FINALREVIEW%20SilviaJoseph_sign.pdf
http://ir.unimas.my/id/eprint/45964/
_version_ 1811600399339094016
score 13.211869

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