Content-based Video Retrieval by Integrating Spatio-Temporal and Stochastic Recognition of Events

As amounts of publicly available video data grow the need to query this data efficiently becomes significant. Consequently content-based retrieval of video data turns out to be a challenging and important problem. We address the specific aspect of inferring semantics automatically from raw video data. In particular, we introduce a new video data model that supports the integrated use of two different approaches for mapping low-level features to high-level concepts. Firstly, the model is extended with a rule-based approach that supports spatio-temporal formalization of high-level concepts, and then with a stochastic approach. Furthermore, results on real tennis video data are presented, demonstrating the validity of both approaches, as well us advantages of their integrated us

Ordered Preference Elicitation Strategies for Supporting Multi-Objective Decision Making

In multi-objective decision planning and learning, much attention is paid to producing optimal solution sets that contain an optimal policy for every possible user preference profile. We argue that the step that follows, i.e, determining which policy to execute by maximising the user's intrinsic utility function over this (possibly infinite) set, is under-studied. This paper aims to fill this gap. We build on previous work on Gaussian processes and pairwise comparisons for preference modelling, extend it to the multi-objective decision support scenario, and propose new ordered preference elicitation strategies based on ranking and clustering. Our main contribution is an in-depth evaluation of these strategies using computer and human-based experiments. We show that our proposed elicitation strategies outperform the currently used pairwise methods, and found that users prefer ranking most. Our experiments further show that utilising monotonicity information in GPs by using a linear prior mean at the start and virtual comparisons to the nadir and ideal points, increases performance. We demonstrate our decision support framework in a real-world study on traffic regulation, conducted with the city of Amsterdam.Comment: AAMAS 2018, Source code at https://github.com/lmzintgraf/gp_pref_elici

Detection of a 1258 Hz high-amplitude kilohertz quasi-periodic oscillation in the ultra-compact X-ray binary 1A 1246-588

We have observed the ultra-compact low-mass X-ray binary (LMXB) 1A 1246-588 with the Rossi X-ray Timing Explorer (RXTE). In this manuscript we report the discovery of a kilohertz quasi-periodic oscillation (QPO) in 1A 1246-588. The kilohertz QPO was only detected when the source was in a soft high-flux state reminiscent of the lower banana branch in atoll sources. Only one kilohertz QPO peak is detected at a relatively high frequency of 1258+-2 Hz and at a single trial significance of more than 7 sigma. Kilohertz QPOs with a higher frequency have only been found on two occasions in 4U 0614+09. Furthermore, the frequency is higher than that found for the lower kilohertz QPO in any source, strongly suggesting that the QPO is the upper of the kilohertz QPO pair often found in LMXBs. The full-width at half maximum is 25+-4 Hz, making the coherence the highest found for an upper kilohertz QPO. From a distance estimate of ~6 kpc from a radius expansion burst we derive that 1A 1246-588 is at a persistent flux of ~0.2-0.3 per cent of the Eddington flux, hence 1A 1246-588 is one of the weakest LMXBs for which a kilohertz QPO has been detected. The root-mean-square (rms) amplitude in the 5-60 keV band is 27+-3 per cent, this is the highest for any kilohertz QPO source so far, in line with the general anti-correlation between source luminosity and rms amplitude of the kilohertz QPO peak identified before. Using the X-ray spectral information we produce a colour-colour diagram. The source behaviour in this diagram provides further evidence for the atoll nature of the source.Comment: 4 pages, 3 figures, accepted for publication in MNRA