An Efficient Text-Based Image Retrieval Using Natural Language Processing (NLP) Techniques

Abstract

The image retrieval system is a computer system for browsing, searching and retrieving images from a large database of digital images or text. Most traditional and common methods of image retrieval utilize some method of adding metadata such as captioning, keywords or descriptions to the images so that retrieval can be performed over the annotation words. Manual image annotation is time-consuming, laborious and expensive. To address this, there has been a large amount of research done on automatic image annotation. With the rapid development of information technology, the number of electronic documents and digital content within documents exceeds the capacity of manual control and management. The usage of images is increased in real time. So, the proposed system concentrates on retrieving image by using the text-based image retrieval system. Text documents are given as input to the preprocessing stage, and features are extracted using TF-IDF. Finally, document clustering method can be used to automatically group the retrieved documents into list of meaningful categories. Document clustering clusters the document of different domains and latent Dirichlet allocation (LDA), each document may be viewed as a mixture of various topics where each document is considered to have a set of topics, and after that relevant documents are retrieved and then images of those relevant documents are retrieved.