Solid Domestic Waste classification using Image Processing and Machine Learning

Daniel Otero Gomez and Mauricio Toro

No yzcfk, OSF Preprints from Center for Open Science

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.

Date: 2021-06-07
New Economics Papers: this item is included in nep-big and nep-isf
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Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:yzcfk

DOI: 10.31219/osf.io/yzcfk

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