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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)
Classifiers used in image processing and computer vision are frequent subject of research and exploitation in applications. This contribution does not directly involve research in the classification itself but rather introduces a systematic approach of evaluation of image classifiers, comparison between the classifiers, and "tuning" the classifiers for(More)
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