A Comparative Analysis of Feature-Based and Deep Learning Approaches for Image Classification

Abstract

Tremendous numbers of images and videos are generated throughout the life cycle of construction projects, offering rich visual data that, if analysed effectively, can support automation in progress monitoring, safety management, quality control, and productivity tracking. Within this context, image classification plays a foundational role: it is the supervised task of mapping an image to one of K semantic categories by learning from labelled examples, requiring the extraction of discriminative visual structure and the production of calibrated class predictions. Despite major advances from handcrafted feature pipelines to Convolutional Neural Networks (CNNs), fair comparison across methods remains challenging due to inconsistent preprocessing, dataset splits, training budgets, and reporting standards. This paper introduces a unified and fully reproducible benchmarking framework that standardises preprocessing, enforces fixed train–test splits, aligns training configurations, and evaluates models using a consistent multi-metric suite. Under identical experimental conditions, we benchmark four paradigm families: classical machine learning (ML) models, handcrafted feature-based methods (Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP), Bag-of-Visual-Words (BoVW) [Formula: see text] Support Vector Machine (SVM)), and CNNs (LeNet-5, ResNet-18), across three datasets (MNIST, Fashion-MNIST, CIFAR-10). We formalise image classification mathematically as a supervised learning problem and assess performance using accuracy, macro-averaged [Formula: see text]1-score, and macro-averaged ROC–AUC, complemented by learning-curve and robustness analyses. Results show that ResNet-18 achieves the strongest performance on all datasets (98.95%, 90.27%, 72.90%), SVM with a Radial Basis Function (RBF) kernel remains competitive on grayscale benchmarks, and hand-crafted pipelines degrade sharply on CIFAR-10. ROC–AUC analysis further highlights calibration–accuracy discrepancies, such as Naïve Bayes achieving high AUC but modest accuracy on MNIST. These findings emphasise the importance of task-aware model selection and demonstrate that compute-aligned, multi-metric benchmarking is essential for fair and reproducible cross-paradigm evaluation. All CNN results are reported under a 50-epoch training budget, which represents a lower bound on converged performance; future extensions will incorporate domain-specific datasets and pre-trained architectures to broaden practical applicability.