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

Image-based window detection: an overview

Automated segmentation of buildings’ façade and detection of its elements is of high relevance in various fields of research as it, e. g., reduces the effort of 3 D reconstructing existing buildings and even entire cities or may be used for navigation and localization tasks. In recent years, several approaches were made concerning this issue. These can be mainly classified by their input data which are either images or 3 D point clouds. This paper provides a survey of image-based approaches. Particularly, this paper focuses on window detection and therefore groups related papers into the three major detection strategies. We juxtapose grammar based methods, pattern recognition and machine learning and contrast them referring to their generality of application. As we found out machine learning approaches seem most promising for window detection on generic façades and thus we will pursue these in future work