ImmEx: IMMersive text documents exploration system

Abstract

Common search engines, especially web-based, rely on standard keyword-based queries and matching algorithms using word frequencies, topics recentness, documents authority and/or thesauri. However, even if those systems present efficient retrieval algorithms, they are not able to lead the user into an intuitive exploration of large data collections because of their cumbersome presentations of the results (e.g. large lists of entries). Moreover, these methods do not provide any mechanism to retrieve other relevant information associated to those contents and, even if query refinement methods are proposed, it is really hard to express it because of the user's inexperience and common lack of familiarity with terminology. Therefore, we propose ImmEx, a novel visual navigational system for an immersive exploration of text documents that overcomes these problems by leveraging the intuitiveness of semantically-related images, retrieved in real-time from popular image sharing services. ImmEx lets independently explore large text collection through a novel approach that exploits the directness of the images and their user-generated metadata. We finally analyze the efficiency and usability of the proposed system by providing case and user studies.