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| dc.contributor.author | Arega, Mulu Mezemir | |
| dc.date.accessioned | 2023-07-04T07:28:56Z | |
| dc.date.available | 2023-07-04T07:28:56Z | |
| dc.date.issued | 2023-03 | |
| dc.identifier.uri | http://ir.bdu.edu.et/handle/123456789/15453 | |
| dc.description.abstract | Habesha women’s dress is one of the clothing cultures that women wear in most areas of Ethiopia, and it is particularly common in the Amhara region. Although the embroidery design of Habesha women’s dress appears to be the same, it has a different texture, shape, and color. This embroidery design is represented by the Awi, BahirDarZege, Sekota, SemenShewa, RayaKobo, Lalibela, Wollo, Gojjam, and Gondar Habesha women’s dress cultures dressing styles. However, as fashion design on clothing develops, the Habesha women’s dress pure cultural embroidered design is replaced with a different fashion style, and the Habesha women’s dress embroidery design cultural identity is lost. There is a lot of research on clothing patterns and color detection, recognition, and classification. However, it's challenging to classify clothes with the same pattern, texture, and color into various classes due to the problem of uncontrol environment, segmentation of ROI, color space, and feature extraction of the embroidery design of Habesha women’s dress. The goal of this study was to use an ensemble feature extraction approach to identify those patterns. A total of 8100 Habesha women’s dress embroidery image were collected from different particular embroidery designer for different cultural clothe dress. We have used 900 images for every nine classes. After getting the images, we applied different image pre-processing techniques. Also, we have done feature extraction, by applying CNN, SGAN, and GLCM. For classification, we have used SVM as a linear kernel. We have developed and trained models such as CNN + GLCM (accuracy: 95.80%), SGAN + GLCM (accuracy: 97.84%), CNN + SGAN (accuracy: 97.96%) and SGAN + GLCM + CNN (accuracy: 98.52%) with PCA. All models have used linear kernel SVM classifiers. Finally, an ensemble of SGAN + GLCM + CNN with the SVM classifier achieved 98.52% accuracy. This combined feature extractor performance has better accuracy than the individual for identifying Habesha women’s dress embroidery design. Keywords – CNN, Embroidery, GLCM, Habesha women’s dress, PCA, SGAN | en_US |
| dc.language.iso | en_US | en_US |
| dc.subject | Electrical and Computer Engineering | en_US |
| dc.title | Habesha Women’s Dress Embroidery Design Identification Using an Ensemble Feature Extraction Approach | en_US |
| dc.type | Thesis | en_US |
