Florida International University and University of Miami TRECVID 2010 - Semantic Indexing

This paper presents the framework and results of team Florida International University - University of Miami (FIU-UM) for the semantic indexing task 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 on the key framebased low-level features (KF) and use co-occurrence probability re-ranking method (RERANK) to generate the final ranked results. • F A FIU-UM-2 2: LF+KF+SF+RERANK - apply subspace learning and classification on the 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 key frame based model and shot based model. Finally, a Late Fusion (LF) step 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 on combined features from the selected key frame-based low-level features (KF) and shot based low-level features (SF) in the Early Fusion (EF) step. Then co-occurrence probability re-ranking method (RERANK) is used. • F A FIU-UM-4 4: SF+RERANK - learning and classification based on shot based low-level features (SF). Then co-occurrence probability re-ranking method (RERANK) is used. From the results of different runs, it can be observed thatF A FIU-UM-1 1 and F A FIU-UM-3 3 have better performance thanF A FIU-UM-2 2 and F A FIU-UM-4 4. It implies that adding features

[1]  Cordelia Schmid,et al.  Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study , 2006, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06).

[2]  Shu-Ching Chen,et al.  Collateral Representative Subspace Projection Modeling for Supervised Classification , 2006, 2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06).

[3]  Daniel Riccio,et al.  A New Data Normalization Function for Multibiometric Contexts: A Case Study , 2008, ICIAR.

[4]  Chao Chen,et al.  Supervised multi-class classification with adaptive and automatic parameter tuning , 2009, 2009 IEEE International Conference on Information Reuse & Integration.

[5]  Lior Rokach,et al.  Data Mining And Knowledge Discovery Handbook , 2005 .

[6]  Xindong Wu,et al.  Discretization Methods , 2010, Data Mining and Knowledge Discovery Handbook.

[7]  Jorma Rissanen,et al.  Minimum Description Length Principle , 2010, Encyclopedia of Machine Learning.

[8]  Tao Mei,et al.  Graph-Based Pairwise Learning to Rank for Video Search , 2009, MMM.

[9]  Shu-Ching Chen,et al.  Florida International University and University of Miami TRECVID 2008 - High Level Feature Extraction , 2008, TRECVID.

[10]  Michael R. Lyu,et al.  A Multimodal and Multilevel Ranking Scheme for Large-Scale Video Retrieval , 2008, IEEE Transactions on Multimedia.

[11]  M. Greenacre,et al.  Multiple Correspondence Analysis and Related Methods , 2006 .

[12]  Shu-Ching Chen,et al.  Correlation-Based Video Semantic Concept Detection Using Multiple Correspondence Analysis , 2008, 2008 Tenth IEEE International Symposium on Multimedia.

[13]  Dennis Koelma,et al.  The MediaMill TRECVID 2008 Semantic Video Search Engine , 2008, TRECVID.

[14]  Xiaohua Zhai,et al.  PKU-ICST at TRECVID 2009: High Level Feature Extraction and Search , 2009 .