Indoor scene classification using machine learning on object-detection based features

Name: Simon Yosboon

keyword: Machine Learning Algorithms

MeSH: Deep learning (Machine learning) -- 294402

Classification :.DDC: e-thesis

; Computational Modeling

MeSH: Training

MeSH: Computer algorithms

MeSH: Convolutions (Mathematics) -- 175259

LCSH: Deep learning (Machine learning) -- 294402

LCSH: Training

LCSH: Computer algorithms

LCSH: Convolutions (Mathematics) -- 175259

Abstract: The classification of scenes from images is a fundamental task in computer vision, vital for various applications ranging from autonomous driving to surveillance systems. An ongoing challenge in this field is the identification of discriminative features for accurate classification. This study addresses this challenge by comparing the effectiveness of two approaches: object-based feature extraction and deep learning. We propose a novel methodology that leverages YOLOv3, a state-of-the-art pre-trained model for object detection, to extract object-based features from scene images. By utilizing YOLOv3, we obtain feature vectors representing the presence and characteristics of objects within each scene. These features are then used as input for four distinct machine learning algorithms to classify scenes. Concurrently, we develop a deep learning model using the original images, which typically requires more computational resources and time for training. We conduct comprehensive experiments to evaluate the performance of both approaches across various scene classification tasks. Surprisingly, our results demonstrate that simple machine learning models utilizing object-level features achieve comparable performance to deep learning methods. This finding suggests that focusing on object-based representations can effectively classify scenes while circumventing the resource-intensive nature of deep learning algorithms.

Mae Fah Luang University. Learning Resources and Educational Media Centre

Address: CHIANG RAI

Email: library@mfu.ac.th

Name: Khwunta Kirimasthong

Role: Advisor

Created: 2024

Modified: 2024-09-30

Issued: 2024-09-30

วิทยานิพนธ์/Thesis

application/pdf

CallNumber: e-thesis

eng

DegreeName: Master of Engineering

Level: Master's Degree

Descipline: Computer Engineering

Grantor: Mae Fah Luang University

©copyrights Mae Fah Luang University

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