Analyzing Deep Learning Techniques in Natural Scene Image Classification
Athanasios Kanavos, Orestis Papadimitriou, Khalil Al-Hussaeni, Ioannis Karamitsos, Manolis Μaragoudakis · 2024
Image classification is a fundamental task in computer vision, with wide applications including autonomous navigation, content recommendation, and environmental monitoring. This paper offers a comprehensive comparative analysis of deep learning techniques using the Intel Natural Scenes Image dataset, which features diverse scenes such as forests, mountains, seas, streets, buildings, and glaciers. Our study evaluates the performance of various Convolutional Neural Network (CNN) architectures, focusing on aspects such as network design, hyperparameters, data augmentation, and transfer learning strategies. We describe our experimental setup in detail, including the CNN models used, preprocessing techniques applied, and evaluation metrics employed. The results and discussions present key findings, highlighting the strengths and limitations of the approaches studied and providing guidance for future research and practical applications. Our systematic analysis yields valuable insights into effective strategies for recognizing natural scenes.