TokyoTech’s TRECVID2006 Notebook

Abstract

In this notebook we describe our TRECVID 2006 experiments. We TokyoTech team participated in shot boundary detection and high-level feature extraction tasks. 1 Shot Boundary Detection Our approach to shot bondary detection uses SVMs with generic features. Using the radial kernel for SVMs, we ignore the difference among the types of gradual transitions (i.e. FOI, DIS, and OTH). We classify shot boundaries into the following three categories. • Cuts(CUT) • Gradual transitions with five frames or less (Short Gradual; SG) • Gradual transitions with more than five frames (Long Gradual; LG) We prepare a kernel function and a feature set for each of these categories. 1.1 Cut Detection Since shot boundaries with less than five frames are classified as “cuts ” in the TRECVID evaluation, the results for SG are added to the results in CUT, and are submitted as the results for “cuts”. For the cut detection, we use two linear kernel SVMs (one for CUT and the other for SG) with different feature sets. The features for a CUT-SVM are activity ratio (the ratio of “dynamic ” pixels to all pixels, wher