Zarathustra’s Cave

In 'Zarathustra’s Cave' the iconic apartment set from 90’s sitcom 'Seinfeld' is presented devoid of actors or action of any kind. Instead the ‘apartment’ sits empty, accompanied by the ambient noise of the screen-space and the distant sound of city traffic. At irregular intervals this relative silence is punctuated by the laughter of an off-screen audience. Unprompted by any on-screen action, this spontaneous audience response ranges from raucous fits of cheering and applause to singular guffaws and giggles. The work is the product of a deep engagement with its subject matter, the result of countless hours of re-watching and editing to isolate the aural and visual spaces presented on the screen. In its resolute emptiness, the installation addresses the notion of narrative expectation. It creates a ‘nothing-space’, where a viewer can experientially oscillate between a sense of presence and absence, tension and pathos, or even between humour and existential crisis. The work was first exhibited in ‘NEW14’, at the Australian Centre for Contemporary Art in Melbourne.

A stunning experience

‘A Stunning Experience’ pits Oprah Winfrey and David Letterman against each other in an infinite interview. Through simple editing and compositing, both parties are rendered mute, positioned at either edge of a black void. The interviewer/interviewee dynamic is playfully reconfigured into an awkward exchange comprised entirely of blinks, breaths and head-nods. Accompanied by the sound of a curious and whispering studio audience, this wordless clash between the two talk show titans becomes emblematic of their infamous sixteen-year feud. The quasi-staring contest upends the cult of personality surrounding these iconic television hosts, while also creating and proposing a space for reflection upon the experiential and even meditative potential of watching television.

Image, Political

Political communication scholars, journalists, and political actors alike, argue that the political process, and deliberative democracy (democracy founded on informed discussion inclusive of citizens), have lost their rational authenticity in that image and media spectacle have become more central to public opinion formation and electoral outcomes than policy. This entry examines the validity of that perception, and the extent to which “image” has emerged as a more significant factor in the political process. And if image is so important in political culture, what the impacts might be on the functioning of democratic processes.

Beyond Gauss: Image-set matching on the Riemannian manifold of PDFs

State-of-the-art image-set matching techniques typically implicitly model each image-set with a Gaussian distribution. Here, we propose to go beyond these representations and model image-sets as probability distribution functions (PDFs) using kernel density estimators. To compare and match image-sets, we exploit Csiszar´ f-divergences, which bear strong connections to the geodesic distance defined on the space of PDFs, i.e., the statistical manifold. Furthermore, we introduce valid positive definite kernels on the statistical manifold, which let us make use of more powerful classification schemes to match image-sets. Finally, we introduce a supervised dimensionality reduction technique that learns a latent space where f-divergences reflect the class labels of the data. Our experiments on diverse problems, such as video-based face recognition and dynamic texture classification, evidence the benefits of our approach over the state-of-the-art image-set matching methods.

Efficient content-based image retrieval based on multi-feature fusion

The increased availability of image capturing devices has enabled collections of digital images to rapidly expand in both size and diversity. This has created a constantly growing need for efficient and effective image browsing, searching, and retrieval tools. Pseudo-relevance feedback (PRF) has proven to be an effective mechanism for improving retrieval accuracy. An original, simple yet effective rank-based PRF mechanism (RB-PRF) that takes into account the initial rank order of each image to improve retrieval accuracy is proposed. This RB-PRF mechanism innovates by making use of binary image signatures to improve retrieval precision by promoting images similar to highly ranked images and demoting images similar to lower ranked images. Empirical evaluations based on standard benchmarks, namely Wang, Oliva & Torralba, and Corel datasets demonstrate the effectiveness of the proposed RB-PRF mechanism in image retrieval.