TRECVID 2005 by NUS PRIS.
Tat‐Seng Chua, Shi-Yong Neo, Hai-Kiat Goh, Ming Zhao, Yang Xiao, Gang Wang · 2005
We participated in the high-level feature extraction and search task for TRECVID 2005. For the high-level feature extraction task, we make use of the available collaborative annotation results for training, and develop 2 methods to perform automated concept annotation: (a) a ranked-Maximal Figure-of-Merit (MFoM) method; and (b) a multimodal rankBoost fusion method. We submitted a total of 7 runs based on these two methods. For the search task, we focus on improving our previous retrieval system by utilizing an event entity model derived from relevant external resources. In addition, we also make use of the various high-level feature extraction results contributed by various participating groups to help in the re-ranking step. We submitted a total 6 runs in the automated search category. The e valuation results show that our event-based approach is effective in human/event queries and that the high-level features is useful for general queries. 1. HIGH LEVEL FEATURE EXTRACTION TASK We explore two methods to perform high-level feature extraction. The first is based on a ranked-Maximal Figure-of-Merit (MFoM) method that has been successfully employed in text categorization. The second employs HMM for high-level features extraction, follow by rankBoost fusion to fuse with other modality features. 1.1 Ranked Maximal Figure-of-merit (MFoM)