Live Mobile Collaboration for Video Production

Design, Guidelines, and Requirements, Personal and Ubiquitous Computing, 2012
Live Mobile Collaboration for Video Production
Marco deSa, David Shamma, Elizabeth Churchill

Traditional cameras and video equipment are gradually losing the race with smart-phones and small mobile devices that allow video, photo and audio capturing on the go. Users are now quickly creating movies and taking photos whenever and wherever they go, particularly at concerts and events.

Still, in-situ media capturing with such devices poses constraints to any user, especially amateur ones. In this paper, we present the design and evaluation of a mobile video capture suite that allows for cooperative ad-hoc production. Our system relies on ad-hoc in-situ collaboration offering users the ability to switch between streams and cooperate with each other in order to capture better media with mobile devices.

Our main contribution arises from the description of our design process focusing on the prototyping approach and the qualitative analysis that followed. Furthermore, we contribute with lessons and design guidelines that emerged and apply to in-situ design of rich video collaborative experiences and with the elicitation of functional and usability requirements for collaborative video production using mobile devices.

Another publication from the same category: Machine Learning and Data Science

IEEE Computing Conference 2018, London, UK

Regularization of the Kernel Matrix via Covariance Matrix Shrinkage Estimation

The kernel trick concept, formulated as an inner product in a feature space, facilitates powerful extensions to many well-known algorithms. While the kernel matrix involves inner products in the feature space, the sample covariance matrix of the data requires outer products. Therefore, their spectral properties are tightly connected. This allows us to examine the kernel matrix through the sample covariance matrix in the feature space and vice versa. The use of kernels often involves a large number of features, compared to the number of observations. In this scenario, the sample covariance matrix is not well-conditioned nor is it necessarily invertible, mandating a solution to the problem of estimating high-dimensional covariance matrices under small sample size conditions. We tackle this problem through the use of a shrinkage estimator that offers a compromise between the sample covariance matrix and a well-conditioned matrix (also known as the "target") with the aim of minimizing the mean-squared error (MSE). We propose a distribution-free kernel matrix regularization approach that is tuned directly from the kernel matrix, avoiding the need to address the feature space explicitly. Numerical simulations demonstrate that the proposed regularization is effective in classification tasks.