Development of VGG-16 transfer learning framework for geographical landmark recognition

Article type: Research Article

Authors: Bansal, Kanishk | Singh, Amar^*

Affiliations: Department of Computer Applications, Lovely Professional University, Phagwara, Punjab, India

Correspondence: [*] Corresponding author: Amar Singh, epartment of Computer Applications, Lovely Professional University, Phagwara, Punjab 144411, India. E-mail: [email protected].

Abstract: Computer vision mandates the development of landmark recognition paradigms for efficient Image Recognition. In this article, the concept of Visual Geometry Group Network (VGG-16) transfer learning is used to develop an AI model over a geographical landmarks’ dataset. The dataset is a small part of Google Landmarks dataset V2 which originally consists of over 4M images. A VGG-16 model trained on ImageNet dataset is used to transfer knowledge. A positive transfer of knowledge is seen and it was observed that the trained model was a highly generalized model for our dataset. Not only a training accuracy of more than 0.85 is observed but equivalent validation accuracy suggests that the developed model is highly robust with minimal overfitting. A comparison of our proposed approach was made with classical machine learning techniques like KNN (K Nearest Neighbor), Decision Trees, Random Forest, SVM (Support Vector Machines) and a 3 Layered CNN. The results clearly depict that the proposed approach outperforms all other machine learning classifiers in consideration.

Keywords: Geographical landmark recognition, VGG-16, transfer learning, data augmentation, computer vision

DOI: 10.3233/IDT-230048

Journal: Intelligent Decision Technologies, vol. 17, no. 3, pp. 799-810, 2023