Open Access

ARTICLE

Enhanced Scene Recognition via Multi-Model Transfer Learning with Limited Labeled Data

Samia Allaoua Chelloug^1,*, Ahmed A. Abd El-Latif^2,3,*, Samah AlShathri¹, Mohamed Hammad^2,4

1 Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
2 EIAS Data Science Lab, College of Computer and Information Sciences, and Center of Excellence in Quantum and Intelligent Computing, Prince Sultan University, Riyadh, 11586, Saudi Arabia
3 Department of Mathematics and Computer Science, Faculty of Science, Menoufia University, Shebin El-Koom, 32511, Egypt
4 Department of Information Technology, Faculty of Computers and Information, Menoufia University, Shibin El Kom, 32511, Egypt

* Corresponding Authors: Samia Allaoua Chelloug. Email: ; Ahmed A. Abd El-Latif. Email:

Computers, Materials & Continua 2026, 87(2), 51 https://doi.org/10.32604/cmc.2026.074485

Received 12 October 2025; Accepted 04 January 2026; Issue published 12 March 2026

Abstract

Scene recognition is a critical component of computer vision, powering applications from autonomous vehicles to surveillance systems. However, its development is often constrained by a heavy reliance on large, expensively annotated datasets. This research presents a novel, efficient approach that leverages multi-model transfer learning from pre-trained deep neural networks—specifically DenseNet201 and Visual Geometry Group (VGG)—to overcome this limitation. Our method significantly reduces dependency on vast labeled data while achieving high accuracy. Evaluated on the Aerial Image Dataset (AID) dataset, the model attained a validation accuracy of 93.6% with a loss of 0.35, demonstrating robust performance with minimal training data. These results underscore the viability of our approach for real-time, data-efficient scene recognition, offering a practical and cost-effective advancement for the field.

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Keywords

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