Nikon Multimedia Event Detection System
Takeshi Matsuo, Shinichi Nakajima · 2010
This note presents Nikon's approach to the multimedia event detection (MED) task of TRECVID 2010. We explain the basic concept of our system which detects events by multiple keyframes extrac-tion based on scene length. We describe the algorithm in detail and show experimental results with the MED dataset. Our simple system got the third place among seven teams of participants. 1 Basic Concept We rely on the assumption that a small number of images in a given video contains enough information for event detection. With this assumption, we reduce the event detection task to the classification problem for a set of images, which we call keyframes, by sampling images that potentially represent necessary information. The keyframes extraction is based on a scene cut detection technique, and the assumption that the longer a scene is, the more relevant information it contains. The classification step employs the bag-of-words (BoW) framework [1] based on the SIFT descriptor [2]. The obtained histogram is fed into the support vector machine (SVM), which is trained over the training dataset for each event category. We refer to our system as Nikon multimedia event detection (MED) system in this note.