IVA-NLPR-IA-CAS TRECVID 2009: High Level Features Extraction
Jinqiao Wang, Si Liu, Chao Chu Liang, Hanqing Lu · TRECVID · 2009
In this report, we present overview and comparative analysis of our HLF detection system. Our baseline method utilizes global and local feature and reaches MAP 0.162. As many concepts in trecvid competition are relevant, we can mine relations between them to help train robust classifier. The last run is to utilize images from web data to enhance high level feature training. Three runs are submitted as following: A_IVA_NLPR_IA_CAS1_1: baseline appraoch– average fusion of 4 SVM classification results for each concept using various feature representation choices. A_IVA_NLPR_IA_CAS2_2: visual concept network based approach. C_IVA_NLPR_IA_CAS3_3: web data transfer based approach.