INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE
ISSN: 2692-5206, Impact Factor: 12,23
American Academic publishers, volume 05, issue 03,2025
Journal:
https://www.academicpublishers.org/journals/index.php/ijai
page 202
COMPARING CLASSICAL AND DEEP LEARNING APPROACHES
IN COMPUTER VISION
Alibek Olimov Ulugbekovich
New Uzbekistan University Faculty of School computing
student of 4 - course of Software engineering
Abstract:
Computer vision has evolved significantly, transitioning from classical techniques
based on handcrafted features to deep learning models that learn representations automatically.
This article compares classical and deep learning approaches in terms of feature extraction,
model complexity, computational efficiency, and application performance. While classical
methods like Scale-Invariant Feature Transform (SIFT), Histogram of Oriented Gradients (HOG),
and edge detection have been effective in controlled environments, deep learning models,
particularly Convolutional Neural Networks (CNNs), have demonstrated superior adaptability in
complex real-world scenarios. The discussion highlights their advantages, limitations, and future
directions in computer vision research.
Keywords:
Computer vision, classical methods, deep learning, CNN, feature extraction, image
processing, SIFT, HOG, Res Net, AI.
Introduction:
Computer vision (CV) is a subfield of artificial intelligence (AI) that enables
machines to interpret and process visual data, mimicking human vision. Early CV systems
depended on handcrafted features and rule-based algorithms, requiring significant domain
expertise. However, with the advent of deep learning, models can now learn representations
automatically, significantly improving performance in tasks like image classification, object
detection, and segmentation. This article compares classical and deep learning approaches,
highlighting their differences in methodology, efficiency, and real-world applications (1,97). The
discussion aims to provide insights into when classical methods remain viable and when deep
learning offers a clear advantage.
Classical Computer Vision Approaches
.
Classical computer vision primarily relies on
feature engineering and traditional machine learning algorithms to analyze visual data.
Feature Extraction Methods. Feature extraction is crucial in classical computer vision. Some of
the most widely used techniques include:
SIFT (Scale-Invariant Feature Transform) – A technique that detects key points and extracts
invariant descriptors useful for object recognition and image matching.
HOG (Histogram of Oriented Gradients) – Commonly used in pedestrian detection, HOG
captures edge orientations in an image, making it effective for structured objects.
Canny Edge Detection – A popular edge detection algorithm that identifies boundaries in an
image using gradients and non-maximum suppression.
INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE
ISSN: 2692-5206, Impact Factor: 12,23
American Academic publishers, volume 05, issue 03,2025
Journal:
https://www.academicpublishers.org/journals/index.php/ijai
page 203
These methods allow machines to extract useful information from images before applying
classification or recognition algorithms.
Machine Learning-Based Classical Methods. Once features are extracted, they are fed into
traditional machine learning models for classification or regression. Popular models include:
Support Vector Machines (SVM) – Works well for binary classification tasks and is often used
in image recognition.
Random Forests – A collection of decision trees that provide robustness against overfitting.
K-Nearest Neighbors (KNN) – A simple but effective algorithm for pattern recognition based
on feature similarity (2,125).
Limitations of Classical Approaches. Despite their usefulness, classical methods have
limitations:
Require manual feature engineering, which demands domain expertise.
Struggle with large and complex datasets due to lack of scalability.
Perform poorly in uncontrolled environments with variations in lighting, perspective, and
occlusion.
Deep Learning Approaches in Computer Vision. Deep learning has revolutionized computer
vision by automating feature extraction and enabling end-to-end learning.
Key Architectures in Deep Learning
Several deep learning architectures have played a crucial role in computer vision:
1.LeNet-5 – One of the earliest CNNs designed for handwritten digit recognition.
2.AlexNet – A deep CNN that won the 2012 ImageNet competition, demonstrating the power
of deep learning.
3.VGGNet – Introduced deeper networks with small 3x3 filters for better feature extraction.
4.ResNet – Introduced residual connections to address the vanishing gradient problem, allowing
the training of very deep networks.
Advantages of Deep Learning
Automatic Feature Learning – Unlike classical methods, CNNs learn feature representations
automatically.
Scalability – Deep learning models can handle large datasets and complex problems efficiently.
Better Generalization – Deep networks perform well in diverse and real-world applications
(3,89).
INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE
ISSN: 2692-5206, Impact Factor: 12,23
American Academic publishers, volume 05, issue 03,2025
Journal:
https://www.academicpublishers.org/journals/index.php/ijai
page 204
Limitations of Deep Learning
High Computational Cost – Requires powerful GPUs and extensive computational resources.
Data Dependency – Needs large amounts of labeled data for training.
Interpretability Issues – Deep models are often considered black-box systems, making it
difficult to explain their decisions.
Comparative
Analysis
Applications and Case Studies.
Flower Detection (Example from ML Research)
Classical Approach: SIFT + SVM for feature extraction and classification.
Deep Learning Approach: CNN-based model (e.g., Res Net, Efficient Net) for direct image
classification.
Comparison: CNNs outperform classical approaches in terms of accuracy and robustness.
Other Applications. Medical Imaging: Deep learning surpasses classical methods in detecting
tumors and anomalies in medical scans (4,295).
Autonomous Vehicles: Classical edge detection is useful for lane detection, but CNNs provide
comprehensive object recognition and scene understanding.
Facial Recognition: Classical methods use eigenfaces and SIFT, whereas deep learning
employs CNN-based architectures like Face Net (5, 97).
INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE
ISSN: 2692-5206, Impact Factor: 12,23
American Academic publishers, volume 05, issue 03,2025
Journal:
https://www.academicpublishers.org/journals/index.php/ijai
page 205
In this picture visually represents the evolution of Computer vision from Classical method to
Deep learning approaches.
Conclusion:
The evolution of computer vision from classical methods to deep learning has
significantly enhanced the field’s capabilities. Classical approaches, which rely on manually
crafted features and traditional machine learning models, are still valuable for tasks that require
low computational resources and well-defined image structures. These methods perform well in
controlled environments but struggle with scalability and adaptability when applied to large,
diverse datasets (6,345).
INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE
ISSN: 2692-5206, Impact Factor: 12,23
American Academic publishers, volume 05, issue 03,2025
Journal:
https://www.academicpublishers.org/journals/index.php/ijai
page 206
On the other hand, deep learning, particularly convolutional neural networks (CNNs), has
revolutionized computer vision by enabling automatic feature learning and significantly
improving performance in complex tasks like image recognition, object detection, and
segmentation. CNNs have demonstrated superior accuracy, generalization, and robustness,
making them the preferred choice for large-scale applications in autonomous driving, healthcare,
and security systems. However, deep learning is not without challenges - it requires vast amounts
of labeled data, high computational power, and remains a black-box model in many cases,
limiting interpretability.
Both classical and deep learning approaches play essential roles in computer vision.
Classical methods remain relevant for tasks requiring lower computational power and well-
defined feature extraction, whereas deep learning dominates large-scale, complex applications
(7,118). Future research should focus on improving deep learning interpretability and reducing
computational costs, making AI-driven vision systems more accessible and efficient.
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