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Solid Domestic Waste classification using Image Processing and Machine Learning

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  • Otero Gomez, Daniel
  • Toro, Mauricio

Abstract

This research concentrates on a bounded version of the waste image classification problem. It focuses on determining the more useful approach when working with two kinds of feature vectors, one construed using pixel values and the second construed from a Bag of Features (BoF). Several image processing techniques such as object centering, pixel value re scaling and edge filtering are applied. Logistic Regression, K Nearest Neighbors, and Support Vector Machines are used as classification algorithms. Experiments demonstrate that object centering significantly improves models’ performance when working with pixel values. Moreover, it is determined that by generating sufficiently simple data relations the BoF approach achieves superior overall results. The Support Vector Machine achieved a 0.9 AUC Score and 0.84 accuracy score.

Suggested Citation

  • Otero Gomez, Daniel & Toro, Mauricio, 2021. "Solid Domestic Waste classification using Image Processing and Machine Learning," OSF Preprints yzcfk, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:yzcfk
    DOI: 10.31219/osf.io/yzcfk
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    File URL: https://osf.io/download/613f65db079a0a054b017914/
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    Cited by:

    1. Otero Gomez, Daniel & Agudelo, Santiago Cartagena & Cadavid, Santiago Isaza & Toro, Mauricio & Ramirez, Juan Camilo, 2021. "A pipeline for Solid Domestic Waste classification using Computer Vision," OSF Preprints rvzyc, Center for Open Science.

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