A query image as input and returning the N most similar images from a gallery.
This study investigates image classification and retrieval using var- ious deep learning models, including a CNN built from inception
and transfer learning-based models such as ResNet and VGG. Limi- tations and opportunities for future advancement in this discipline
are identified by the research. The size of the dataset is a limita- tion, indicating that a larger dataset is required to enhance model
generalization. Analyzing the evaluation metrics highlights the importance of integrating additional metrics for a comprehensive evaluation. Model selection and architecture justification, as well as the investigation of model interpretability and explainability, are identified as areas for enhancement. The study recommends
potential improvements to fine-tuning strategies, data augmenta- tion techniques, and the investigation of alternative architectures.
In conclusion, the abstract highlights the importance of resolving these limitations and conducting additional research to enhance the performance and comprehension of image classification and retrieval systems.