Television (TV) programs are increasingly augmented with second-screen applications that can provide complementary information and social media interaction related to the program. However, there is a lack of understanding about the optimal design of such applications. In this paper, we study the effects of complementary information and tweets on the media experience indexed by a comprehensive self-report questionnaire. We built a custom second-screen tablet application and conducted an experiment to investigate the effects of complementary information and tweets on the media experience of TV programs. Apart from reducing concentration and immersion, neither the presence of complementary information nor the presence of tweets affected the media experience of the TV programs, whereas the media experience of second-screen content depended on which program it accompanied. Our results demonstrate that the media experience of complementary information and tweets presented on second screens differs between TV programs.

Visual object categorisation (VOC) has become one of the most actively investigated topic in computer vision. In the mainstream studies, the topic is considered as a supervised problem, but recently, the ultimate challenge has been posed: Unsupervised visual object categorisation. Hitherto only a few methods have been published, all of them being computationally demanding successors of their supervised counterparts. In this study, we address this problem with a simple and effective method: competitive learning leading to self organisation (self-categorisation). The unsupervised competitive learning approach is implemented using the Kohonen self-organising map algorithm (SOM). The SOM is used to perform the both unsupervised codebook generation and object categorisation. We present our method in detail and compare results to the supervised approach.

Visual object categorization is one of the most active research topics in computer vision, and Caltech-101 data set is one of the standard benchmarks for evaluating the method performance. Despite of its wide use, the data set has certain weaknesses: (i) the objects are practically in a standard pose and scale in the middle of the images and (ii) background varies too little in certain categories making it more discriminative than the foreground objects. In this work, we demonstrate how these weaknesses bias the evaluation results in an undesired manner. In addition, we reduce the bias effect by replacing the backgrounds with random landscape images from Google and by applying random Euclidean transformations to the foreground objects. We demonstrate how the proposed randomization process makes visual object categorization more challenging improving the relative results of methods which categorize objects by their visual appearance and are invariant to pose changes. The new data set is made publicly available for other researchers.