Abstract
Content-based video retrieval systems have been widely associated with desktop environments that are largely complex in nature, targeting expert users and often require complex queries. Due to this complexity, interaction with these systems can be a challenge for regular ”novice” users. In recent years, a shift can be observed from this traditional desktop environment to that of handheld devices, which requires a different approach to interacting with the user. In this paper, we evaluate the performance of a handheld content-based video retrieval system on both expert and novice users. We show that with this type of device, a simple and intuitive interface, which incorporates the principles of content-based systems, though hidden from the user, attains the same accuracy for both novice and desktop users when faced with complex information retrieval tasks. We describe an experiment which utilises the Apple iPad as our handheld medium in which both a group of experts and novice users run the interactive experiments from the 2010 TRECVid Known-Item Search task. The results indicate that a carefully defined interface can equalise the performance of both novice and expert users.
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Scott, D., Hopfgartner, F., Guo, J., Gurrin, C. (2013). Evaluating Novice and Expert Users on Handheld Video Retrieval Systems. In: Li, S., et al. Advances in Multimedia Modeling. Lecture Notes in Computer Science, vol 7733. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35728-2_7
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DOI: https://doi.org/10.1007/978-3-642-35728-2_7
Publisher Name: Springer, Berlin, Heidelberg
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