Open AccessContextual Image Illustrator: A Review
Author(s) -
Kanika Sharma
Publication year - 2021
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2021.38105
Subject(s) - information retrieval , computer science , ranking (information retrieval) , relevance (law) , newspaper , image (mathematics) , word (group theory) , image retrieval , content (measure theory) , content based image retrieval , natural language processing , artificial intelligence , mathematics , advertising , mathematical analysis , geometry , political science , law , business
Any story or any other literary content is best understood and advertised with the help of pictures. Images are used to arouse reader’s interest and comprehension in the content. The contextual image illustrator will take any content description and will output the ranked images related to that content. The text can be any blog, newspaper article, any story or any other content. The image retrieval process that has been used for this purpose is Text based Image Retrieval, i.e., TBIR. Semantic keywords are extricated from the story; images are looked through an annotated database. Thereafter, an image ranking scheme will determine the relevance of each image. Then the user can choose among the images displayed. A score along with each image will also be displayed representing its relevance to the query. The keywords stemming and stop word removal has been explained in the document. Also, the algorithm that has been designed to determine the score and hence the image’s significance has been calculated. Testing consisting of both unit testing and module testing of the project are explained. Keywords: Keyword Extraction, Image Search, Stemming, Stop word Removal, URL Score, URL Ranking