With recent uptake in the usage of mobile devices, such as smartphones and tablets, increasing at an exponential rate, these devices have become part of everyday life. This high yield of information access comes at a cost. With still limited input metrics, it is prudent to develop content based techniques to filter the amount of content that is returned, for example, from search requests to video search engines. In addition, such handheld devices are used by a highly heterogeneous user community, including people with little or no experience. In this work, we focus on the latter, i.e. such casual users (‘novices’), and target video search and retrieval. We begin by examining new methods of developing related Content-Based Multimedia Information Retrieval systems for novices on handheld tablet devices. We analyze the shortcomings of traditional desktop systems which favor the expert user formulating complex queries and focus on the simplicity of design and interaction on tablet devices. We create and test three prototype demonstrators over three years of the TRECVid known item search task in order to determine the best features and appropriate usage to attain both high quality, usability, and precision from our novice users. In the first experiment, we determine that novice users perform similarly to an expert user group, one major premise of this research. In our second experiment, we analyze methods which can be applied automatically to aid novice users, thus enhancing their search performance. Our final experiment deals with different visualization approaches which can further aid the users. Overall, our results show that each year our systems made an incremental improvement. The 2011 TRECVid system performed best of all submissions in that year, despite the reduced complexity, enabling novice users to perform equally well as experts and experienced searchers.
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