Florida International University and University of Miami TRECVID 2010- Semantic Indexing
Chao Chen, Qiusha Zhu, Dianting Liu, Tao Meng, Lin Lin, Mei‐Ling Shyu, Yimin Yang, Hsin-Yu Ha, Fausto C. Fleites, Shu‐Ching Chen · 2011
The paper presents the framework and results of team Florida International University- University of Miami (FIU-UM) for the task of semantic indexing of TRECVID 2010. In this task, we submitted four runs of results: • F A FIU-UM-1 1: KF+RERANK- apply subspace learning and classification using key framebased low-level features (KF) and co-occurrence probability re-ranking method (RERANK). • F A FIU-UM-2 2: LF+KF+SF+RERANK- apply subspace learning and classification using late fusion (LF), i.e., key frame-based low-level features (KF) and shot based low-level features (SF) separately. Then co-occurrence probability re-ranking method (RERANK) is used for both keyframe based model and shot based model. Finally, a fusion method combines ranking scores from each model and generates the final ranked shots. • F A FIU-UM-3 3: EF+KF+SF+RERANK- apply subspace learning and classification using early fusion (EF), i.e., combined features from the selected key frame-based low-level features (KF) and shot based low-level features (SF). Then co-occurrence probability re-ranking method (RERANK) is used.