Talking it through : the case for the use of conversation analytic approaches in the analysis of news journalism

This study makes out the case for the use of the Conversational Analytic method as a research approach that might both extricate and chronicle the features of the journalism interview. It seeks to encourage such research to help inform understanding of this form and to provide further lessons as to the nature of journalism practice. Such studies might follow many paths but this paper focuses more particularly on the outcomes for the debate as to the continued relevance of "objectivity" in informing journalism professional practice. To make out the case for the veracity of CA as a means through which the conduct of journalism practice might be explored the paper examines: the theories of the interaction order that gave rise to the CA method; outlines the key features of the journalism interview as explicated through the CA approach; outlines the implications of such research for the establishment of the standing of "objectivity". It concludes as to the wider relevance of such studies of journalism practice for a fracturing journalism field, which suffers from a lack of benchmarks to measure the public benefit of the range of forms that now proliferate on the internet.

Kernel analysis on Grassmann manifolds for action recognition

Modelling video sequences by subspaces has recently shown promise for recognising human actions. Subspaces are able to accommodate the effects of various image variations and can capture the dynamic properties of actions. Subspaces form a non-Euclidean and curved Riemannian manifold known as a Grassmann manifold. Inference on manifold spaces usually is achieved by embedding the manifolds in higher dimensional Euclidean spaces. In this paper, we instead propose to embed the Grassmann manifolds into reproducing kernel Hilbert spaces and then tackle the problem of discriminant analysis on such manifolds. To achieve efficient machinery, we propose graph-based local discriminant analysis that utilises within-class and between-class similarity graphs to characterise intra-class compactness and inter-class separability, respectively. Experiments on KTH, UCF Sports, and Ballet datasets show that the proposed approach obtains marked improvements in discrimination accuracy in comparison to several state-of-the-art methods, such as the kernel version of affine hull image-set distance, tensor canonical correlation analysis, spatial-temporal words and hierarchy of discriminative space-time neighbourhood features.