Year: 2008
Type: conference-paper
Abstract: The Tokyo Institute of Technology team participated in the high-level feature extraction, surveillance event detection pilot and Rushes summarization tasks for TRECVID2008. In the high-level feature (HLF) extraction task, we employed a framework using a tree-structured codebook and a node selection technique last year. This year we focused on the position information of each object-related HLF. During the training phase, we applied our method not on the whole key-frame images, but on the regions in the image which contain the annotated HLF only. From the evaluation of TRECVID2008, the inferred average precisions of the three runs are all 0.011. The method we improved this year doesn’t contribute to a better performance. In surveillance event detection pilot task we use optical flow features and an SVM (Support Vector Machine) to detect each surveillance event. We present our preliminary experimental results in this paper. In the rushes summarization task, we estimated the number of scenes for a summary using minimum description length. We use two low-level features, the YCbCr color histogram and optical flow. 1. High-Level Feature extraction In the high-level feature (HLF) extraction task in TRECVID2007, we proposed a novel method, which
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W99808209