Malay festive seasons food recognition for calorie detection / Nurul Hafiza Basiruddin

The idea of adding an auto-recognition feature for Malay Festive Seasons Food based on images is very challenging task in mage computer vision as it is something new and undiscovered before. However, this recognition is important for Malaysian users to manage calorie intake, especially during Hari R...

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Main Author:	Basiruddin, Nurul Hafiza
Format:	Thesis
Language:	English
Published:	2021
Subjects:	Electronic Computers Computer Science Application software Integrated software
Online Access:	https://ir.uitm.edu.my/id/eprint/59371/1/59371.pdf
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id	my-uitm-ir.59371
record_format	uketd_dc
spelling	my-uitm-ir.593712022-07-21T15:39:05Z Malay festive seasons food recognition for calorie detection / Nurul Hafiza Basiruddin 2021-07 Basiruddin, Nurul Hafiza Electronic Computers. Computer Science Application software Integrated software The idea of adding an auto-recognition feature for Malay Festive Seasons Food based on images is very challenging task in mage computer vision as it is something new and undiscovered before. However, this recognition is important for Malaysian users to manage calorie intake, especially during Hari Raya, one of the biggest festive seasons and the most celebrated festivals in Malaysia. As color plays an important role in differentiating the type of food, therefore this research aims to implement Color Feature Extraction Method after performing segmentation techniques during the pre-processing phase where each color from the images will be extracted individually. Then the result from the Color Feature Extraction Method is used to identify the type of food by using Error-Correcting Output Codes (ECOC) classification which is the part of the Support Vector Machine (SVM) algorithm. The reliability and effectiveness of the classifier are evaluated through system testing where the total overall percentage of correct recognition performed by the system is 82.5% according to the correct and wrong recognition obtained. The ability to recognize the food correctly after classifying the image is crucial in this research to accurately perform the calorie estimation whereby the calorie value will be auto- generated after food recognition is performed. Besides, thorough research has been conducted on the calorie value for each type of food by using the reliable internet resources to ensure users can benefit from the system in the future. 2021-07 Thesis https://ir.uitm.edu.my/id/eprint/59371/ https://ir.uitm.edu.my/id/eprint/59371/1/59371.pdf text en public degree Universiti Teknologi MARA, Perak Faculty of Computer and Mathematical Sciences Zulkifli, Zalikha
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Zulkifli, Zalikha
topic	Electronic Computers Computer Science Application software Integrated software
spellingShingle	Electronic Computers Computer Science Application software Integrated software Basiruddin, Nurul Hafiza Malay festive seasons food recognition for calorie detection / Nurul Hafiza Basiruddin
description	The idea of adding an auto-recognition feature for Malay Festive Seasons Food based on images is very challenging task in mage computer vision as it is something new and undiscovered before. However, this recognition is important for Malaysian users to manage calorie intake, especially during Hari Raya, one of the biggest festive seasons and the most celebrated festivals in Malaysia. As color plays an important role in differentiating the type of food, therefore this research aims to implement Color Feature Extraction Method after performing segmentation techniques during the pre-processing phase where each color from the images will be extracted individually. Then the result from the Color Feature Extraction Method is used to identify the type of food by using Error-Correcting Output Codes (ECOC) classification which is the part of the Support Vector Machine (SVM) algorithm. The reliability and effectiveness of the classifier are evaluated through system testing where the total overall percentage of correct recognition performed by the system is 82.5% according to the correct and wrong recognition obtained. The ability to recognize the food correctly after classifying the image is crucial in this research to accurately perform the calorie estimation whereby the calorie value will be auto- generated after food recognition is performed. Besides, thorough research has been conducted on the calorie value for each type of food by using the reliable internet resources to ensure users can benefit from the system in the future.
format	Thesis
qualification_level	Bachelor degree
author	Basiruddin, Nurul Hafiza
author_facet	Basiruddin, Nurul Hafiza
author_sort	Basiruddin, Nurul Hafiza
title	Malay festive seasons food recognition for calorie detection / Nurul Hafiza Basiruddin
title_short	Malay festive seasons food recognition for calorie detection / Nurul Hafiza Basiruddin
title_full	Malay festive seasons food recognition for calorie detection / Nurul Hafiza Basiruddin
title_fullStr	Malay festive seasons food recognition for calorie detection / Nurul Hafiza Basiruddin
title_full_unstemmed	Malay festive seasons food recognition for calorie detection / Nurul Hafiza Basiruddin
title_sort	malay festive seasons food recognition for calorie detection / nurul hafiza basiruddin
granting_institution	Universiti Teknologi MARA, Perak
granting_department	Faculty of Computer and Mathematical Sciences
publishDate	2021
url	https://ir.uitm.edu.my/id/eprint/59371/1/59371.pdf
_version_	1783735030807265280

Malay festive seasons food recognition for calorie detection / Nurul Hafiza Basiruddin

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