Extremal Region Selection for MSER Detection in Food Recognition

Mohd Norhisham Razali @ Ghazali and Noridayu Manshor and Alfian Abdul Halin and Norwati Mustapha and Razali Yaakob (2021) Extremal Region Selection for MSER Detection in Food Recognition. ASM Science Journal, 15. pp. 1-11. ISSN 1823-6782

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Abstract

The visual analysis of foods on social media by using food recognition algorithm provides valuable insight from the health, cultural and marketing. Food recognition offers a means to automatically recognise foods as well the useful information such as calories and nutritional estimation by using image processing and machine learning technique. The interest points in food image can be detected effectively by using Maximally Stable Extremal Region (MSER). As MSER used global segmentation and many food images have a complex background, there are numerous irrelevant interest points are detected. These interest points are considered as noises that lead to computation burden in the overall recognition process. Therefore, this research proposes an Extremal Region Selection (ERS) algorithm to improve MSER detection by reducing the number of irrelevant extremal regions by using unsupervised learning based on the k-means algorithm. The performance of ERS algorithm is evaluated based on the classification performance metrics by using classification rate (CR), error rate (ERT), precision (Prec.) and recall (rec.) as well as the number of extremal regions produced by ERS. UECFOOD-100 and UNICT-FD1200 are the two food datasets used to benchmark the proposed algorithm. The results of this research have found that the ERS algorithm by using optimum parameters and thresholds, be able to reduce the number of extremal regions with sustained classification performance.

Item Type:	Article
Keyword:	Food recognition , Object recognition , Image processing , Machine learning
Subjects:	Q Science > QA Mathematics > QA1-939 Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Department:	FACULTY > Faculty of Social Sciences and Humanities
Depositing User:	SAFRUDIN BIN DARUN -
Date Deposited:	08 Jun 2022 10:04
Last Modified:	08 Jun 2022 10:04
URI:	https://eprints.ums.edu.my/id/eprint/32660

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