An Adaptive Four-factor User Interaction Model for Content-Based Image Retrieval
This event took place on Wednesday 23 September 2009 at 00:00
Haiming Liu
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. Thus, we propose a four-factor user interaction model and investigate its effects on CBIR by an empirical and a user evaluation. Whilst the model was developed for our research purposes, we believe the model could be adapted to any content-based search system.
This event took place on Wednesday 23 September 2009 at 00:00
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. Thus, we propose a four-factor user interaction model and investigate its effects on CBIR by an empirical and a user evaluation. Whilst the model was developed for our research purposes, we believe the model could be adapted to any content-based search system.
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Interacting with other people not only forms the core of human social and psychological experience, but also lies at the centre of what makes the internet such a rich, powerful and exciting collection of knowledge media. We are especially interested in what happens when such interactions take place on a very large scale -- not only because we work regularly with tens of thousands of distance learners at the Open University, but also because it is evident that being part of a crowd in real life possesses a certain 'buzz' of its own, and poses a natural challenge. Different nuances emerge in different user contexts, so we choose to investigate the contexts of work, learning and play to better understand the trade-offs involved in designing effective large-scale social software for multiple purposes.
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