Learn More
In this paper we describe our experiments in all task of TRECVid 2008. This year, we have concentrated mainly on the local (affine covariant) image features and its transformation into a search-able form for the Content-based copy detection pilot together with the indexing and search techniques for the Search task and a practical test of the background(More)
1. The runs: • A_brU_1 – features extracted from each frame; SVM per-frame classifier trained on frames in each shot; simple decision tree judging shots based on per-frame results • A_brV_2 – same as A_brU_1, but SVM trained on all training data (the first run divided the training data to training and cross-validation datasets), with SVM configured from the(More)
This paper describes the video summarization system built for the TRECVID 2007 evaluation by the Brno team. Motivations for the system design and its overall structure are described followed by more detailed description of the critical parts of the system, which are feature extraction and clustering of frames (shots, sub-shots) in time domain. Many ideas(More)
The paper deals with a solution for visual surveillance metadata management. Data coming from many cameras is annotated using computer vision units to produce metadata representing moving objects in their states. It is assumed that the data is often uncertain, noisy and some states are missing. The solution consists of the following three layers: (a) data(More)
In this abstract, we investigate the network traffic that may cause the unauthorized control of a computer in the campus network using buffer overflow attacks, the objective of which is to gain the control of privileged programs and computers. We provide statistics of the network traffic in a campus and an eterprise network together with probabilities of a(More)
In this paper, we propose a framework for interactive, iterative, and intuitive mining of multilevel association, characterization and classification rules on data organized in multi-level conceptual hierarchies. This framework is called OLAM SE (Self Explaining On-Line Analytical Mining) and it is proposed as an extension of OLAP or as an alternative to(More)