Smart content management system

Abstract

In today’s digital age, it is common for individuals to own a digital camera, either as a standalone device or one connected to a mobile phone. With the ability to easily record, edit, store, and distribute high-quality images, as well as the low cost of memory, these factors have greatly contributed to the expansion of personal image archives. This has led to a demand for online image database services, such as Flickr, Facebook, and Instagram. However, a significant portion of these images are not tagged, making them difficult to retrieve through text queries. Similarly, in the commercial sector, still image archives continue to be accumulated, with digitized images being manually tagged and categorized by teams. While automatic content-based annotation methods have seen improvements in accuracy, the sheer quantity of images in real-world applications makes it impractical to manually index them. As a result, there is growing interest in leveraging image annotation algorithms to automatically annotate images. By using content-based image retrieval methods, image-text similarity classifiers, and a neural search engine. This project aims to provide a solution that allows users to retrieve their photos via text queries effectively without having to manually tag each photo individually.