Automated Recyclable Waste Classification using Multiple Shape-based Properties and Quadratic Discriminant
Mas Rina Mustaffa1, Nurul Amelina Nasharuddin2, Masnida Hussin3, Nur Izzahtul Nabilah Mohd Nazri4, Alya Hidayah Zakaria5, Nik Nur Ellya Arisha Nik Ahmad Zamri6
1Mas Rina Mustaffa, Department of Multimedia, Faculty of Computer Science and Information Technology, University Putra Malaysia, Serdang, Selangor, Malaysia.
2Nurul Amelina Nasharuddin, Department of Multimedia, Faculty of Computer Science and Information Technology, University Putra Malaysia, Serdang, Selangor, Malaysia.
3Masnida Hussin, Department of Communication Technology and Network, Faculty of Computer Science and Information Technology, University Putra Malaysia, Serdang, Selangor, Malaysia.
4Nur Izzahtul Nabilah Mohd Nazri, MARA Junior Science College Tun Ghafar Baba, Melaka, Malaysia.
5Alya Hidayah Zakaria, Kota Putra, Terengganu, Malaysia.
6Nik Nur Ellya Arisha Nik Ahmad Zamri, MARA Junior Science College Pengkalan Chepa, Kelantan, Malaysia.
Manuscript received on 18 June 2019 | Revised Manuscript received on 25 June 2019 | Manuscript Published on 19 June 2019 | PP: 270-274 | Volume-8 Issue-8S June 2019 | Retrieval Number: H10450688S19/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Nowadays, a crucial issue in major cities throughout the world is waste management where tons of waste being generated every single day. Fortunately, people can count on other methods to protect the environment through waste recycling. In most countries, waste that can be recycled are being categorised or handled manually by using human labour. The objective of this project is to develop an automated recyclable waste classification method which can replace the traditional ways of dealing with three types of waste, namely plastic bottles, papers, and soda cans. Firstly, we computed a global threshold value based on the Otsu method to obtain a binary image representation. Few morphological operators are then executed to obtain the regions of interest (waste’s object). For feature representation, we calculated multiple shape properties of the waste’s object such as perimeter, area, eccentricity, and major axis length. We experimented the extracted feature vectors with few classifiers. Our findings have shown that the waste classification prototype is able to effectively categorise waste up to 94.4% accuracy based on the proposed shape representation and Quadratic Discriminant classifier.
Keywords: Quadratic Discriminant, Shape Descriptor, Waste Classification.
Scope of the Article: Classification