COMPARING CLASSICAL AND DEEP LEARNING APPROACHES IN COMPUTER VISION

Аннотация

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 methodslikeScale-Invariant Feature Transform (SIFT), Histogram ofOriented 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 computervision 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 aclear advantage. Classical Computer Vision Approaches. Classical computer vision primarily relies on feature engineering and traditional machine learning algorithmsto analyze visual data. Feature Extraction Methods. Feature extraction is crucial in classical computer vision. Some of themostwidely 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 edgeorientations in an image, making it effectivefor structured objects. Canny Edge Detection – A popular edge detection algorithm that identifies boundaries in an image using gradients and non-maximumsuppression. page202 INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE ISSN:2692-5206,ImpactFactor:12,23 AmericanAcademicpublishers,volume05,issue03,2025 Journal: https://www.academicpublishers.org/journals/index.php/ijai 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 machinelearning models forclassification orregression. Popularmodels include: Support Vector Machines (SVM) – Works well for binary classification tasks and is often used inimage recognition. RandomForests –A collection ofdecision trees that provide robustness against overfitting. K-Nearest Neighbors (KNN) – A simple but effective algorithm for pattern recognition based onfeature similarity (2,125). Limitations of Classical Approaches. Despite their usefulness, classical methods have limitations: Requiremanual feature engineering, which demands domain expertise. Strugglewith large and complex datasets due tolack ofscalability. Perform poorly in uncontrolled environments with variations in lighting, perspective, and occlusion. Deep Learning Approaches in Computer Vision. Deep learning has revolutionized computer visionby automating featureextraction and enabling end-to-end learning. Key Architectures inDeep Learning Several deep learning architectures have played acrucial role incomputer vision: 1.LeNet-5 –Oneof theearliest CNNs designed for handwritten digit recognition. 2.AlexNet – A deep CNN that won the 2012 ImageNet competition, demonstrating the power ofdeep learning. 3.VGGNet –Introduced deepernetworks with small3x3filters for better featureextraction. 4.ResNet – Introduced residual connections to address the vanishing gradient problem, allowing thetraining ofvery 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). page203 INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE ISSN:2692-5206,ImpactFactor:12,23 AmericanAcademicpublishers,volume05,issue03,2025 Journal: https://www.academicpublishers.org/journals/index.php/ijai Limitations ofDeep Learning High Computational Cost–Requires powerful GPUs and extensivecomputational resources. DataDependency –Needs large amounts oflabeled datafor training.