Abstract
Information graphics (infographics) in popular media are highly structured knowledge representations that are generally designed to convey an intended message. This paper presents a novel methodology for retrieving infographics from a digital library that takes into account a graphic's structural and message content. The retrieval methodology can be summarized thus: 1) hypothesize requisite structural and message content from a natural language query, 2) measure the relevance of each candidate infographic to the requisite structural and message content hypothesized from the user query, and 3) integrate these relevance measurements via a linear combination model in order to produce a ranked list of infographics in response to the user query. The methodology has been implemented and evaluated, and it significantly outperforms a baseline method that treats queries and graphics as bags of words.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
