Image Search

Abstract

With the rapid increase in digital images, it has become essential to have advanced systems to find specific images quickly from large collections. Traditional methods that depend on text descriptions often fail because tagging images manually is time-consuming and subjective. This project uses deep learning to create an efficient image search system for a dataset of about approximately 5000 printing images.Transfer Learning technique has been implemented in this work. Transfer learning is an ambitious task, but it results in impressive outcomes for identifying distinct patterns in tiny datasets of approximately 5000 images of printing images from our web site ’ARC Print’. The goal is to produced best feature vectors that capture the important details of each image, allowing us to search based on content rather than text. We tested the system for accuracy and speed, showing that it works well and is efficient. Feedback from management also confirms that the system is practical and useful. The results indicate that our method is much better than traditional ones, providing quick and accurate search results based on image content.This project demonstrates the power of deep learning in image search, and it can be used in many areas specially in online shopping. The proposed model achieved 89 % accuracy and based on our findings,the proposed system can help to enhance the user experience on our website far better.In the future, we aim to improve the system further and explore more applications, highlighting the importance of advanced machine learning in handling large collections of images.