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Content-based image retrieval system for solid waste bin level detection and performance evaluation.

M A Hannan¹, M Arebey¹, R A Begum²

¹Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering & Built Environment, Universiti Kebangsaan Malaysia, Bangi, Selangor DE, Malaysia.

Waste Management (New York, N.Y.)

|February 13, 2016

Summary

This summary is machine-generated.

This study introduces a content-based image retrieval (CBIR) system for bin level detection using texture analysis. Earth Mover's Distance (EMD) demonstrated superior accuracy in bin level detection compared to other similarity metrics.

Keywords:

CBIR Feature extraction GLAM GLCM Gabor Solid waste bin level

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Area of Science:

Computer Vision
Image Processing
Machine Learning

Background:

Accurate monitoring of bin levels is crucial for inventory management and waste reduction.
Traditional methods for bin level detection can be inefficient or inaccurate.
Content-Based Image Retrieval (CBIR) offers a promising approach for automated visual analysis.

Purpose of the Study:

To develop and evaluate a CBIR system for detecting bin levels using image texture.
To compare the performance of various similarity distance metrics in this application.
To assess different texture feature extraction techniques for bin level identification.

Main Methods:

Implemented a CBIR system utilizing texture features extracted from bin images.
Employed feature extraction techniques including Gabor wavelet filter, Gray Level Co-occurrence Matrix (GLCM), and Gray Level Aura Matrix (GLAM).
Evaluated similarity distances: Euclidean, Bhattacharyya, Chi-squared, Cosine, and Earth Mover's Distance (EMD).
Performance was quantified using average retrieval rate (precision-recall) and F1 measure.

Main Results:

The EMD distance metric achieved the highest accuracy in bin level detection.
EMD outperformed other tested similarity distances in the CBIR system.
Texture analysis using GLCM and GLAM proved effective for identifying bin levels and surrounding areas.

Conclusions:

The proposed CBIR system effectively detects bin levels through texture analysis.
EMD is a highly effective similarity metric for texture-based image retrieval in this context.
The study validates the potential of CBIR for automated bin level monitoring systems.