Author(s): Delgado, Diogo Miguel Melo
Date: 2010
Persistent ID: http://hdl.handle.net/10362/4478
Origin: Repositório Institucional da UNL
Subject(s): Story illustration; Text analysis; Informational retrieval; Image-text association; Vector space model; User feedback
Submitted in part fulfillment of the requirements for the degree of Master in Computer Science
We all had the problem of forgetting about what we just read a few sentences before. This comes from the problem of attention and is more common with children and the elderly. People feel either bored or distracted by something more interesting. The challenge is how can multimedia systems assist users in reading and remembering stories? One solution is to use pictures to illustrate stories as a mean to captivate ones interest as it either tells a story or makes the viewer imagine one. This thesis researches the problem of automated story illustration as a method to increase the readers’ interest and attention. We formulate the hypothesis that an automated multimedia system can help users in reading a story by stimulating their reading memory with adequate visual illustrations. We propose a framework that tells a story and attempts to capture the readers’ attention by providing illustrations that spark the readers’ imagination. The framework automatically creates a multimedia presentation of the news story by (1) rendering news text in a sentence by-sentence fashion, (2) providing mechanisms to select the best illustration for each sentence and (3) select the set of illustrations that guarantees the best sequence. These mechanisms are rooted in image and text retrieval techniques. To further improve users’ attention, users may also activate a text-to-speech functionality according to their preference or reading difficulties. First experiments show how Flickr images can illustrate BBC news articles and provide a better experience to news readers. On top of the illustration methods, a user feedback feature was implemented to perfect the illustrations selection. With this feature users can aid the framework in selecting more accurate results. Finally, empirical evaluations were performed in order to test the user interface,image/sentence association algorithms and users’ feedback functionalities. The respective results are discussed.
