• Applying information foraging theory to understand user interaction with content-based image retrieval

      Liu, Haiming; Mulholland, Paul; Song, Dawei; Uren, Victoria; Rüger, Stefan (ACM, 2010)
      The paper proposes an ISE (Information goal, Search strategy, Evaluation threshold) user classification model based on Information Foraging Theory for understanding user interaction with content-based image retrieval (CBIR). The proposed model is verified by a multiple linear regression analysis based on 50 users' interaction features collected from a task-based user study of interactive CBIR systems. To our best knowledge, this is the first principled user classification model in CBIR verified by a formal and systematic qualitative analysis of extensive user interaction data.
    • Enabling effective user interactions in content-based image retrieval

      Liu, Haiming; Zagorac, Srđan; Uren, Victoria; Song, Dawei; Ruger, Stefan (Springer-Verlag Berlin Heidelberg, 2009)
      This paper presents an interactive content-based image retrieval framework--uInteract, for delivering a novel four-factor user interaction model visually. The four-factor user interaction model is an interactive relevance feedback mechanism that we proposed, aiming to improve the interaction between users and the CBIR system and in turn users overall search experience. In this paper, we present how the framework is developed to deliver the four-factor user interaction model, and how the visual interface is designed to support user interaction activities. From our preliminary user evaluation result on the ease of use and usefulness of the proposed framework, we have learnt what the users like about the framework and the aspects we could improve in future studies. Whilst the framework is developed for our research purposes, we believe the functionalities could be adapted to any content-based image search framework.
    • A four-factor user interaction model for content-based image retrieval

      Liu, Haiming; Uren, Victoria; Song, Dawei; Ruger, Stefan (Springer-Verlag Berlin Heidelberg, 2009)
      In order to bridge the "Semantic gap", a number of relevance feedback (RF) mechanisms have been applied to content-based image retrieval (CBIR). However current RF techniques in most existing CBIR systems still lack satisfactory user interaction although some work has been done to improve the interaction as well as the search accuracy. In this paper, we propose a four-factor user interaction model and investigate its effects on CBIR by an empirical evaluation. Whilst the model was developed for our research purposes, we believe the model could be adapted to any content-based search system.