Video Retrieval

Video retrieval at TRECVID, from shot boundary detection and concept detection to search.

Back in college, I worked on our team’s entry to TRECVID 2007 (Yuan et al., 2007), an annual benchmark for content-based video retrieval run by the National Institute of Standards and Technology (NIST). The system covered three tasks. For shot boundary detection, it combined block-based color histograms with a motion detector that suppressed false alarms from camera and object motion, followed by a SIFT-based post-processing step. For high-level feature extraction (i.e., concept detection), it learned concept distributions over image regions with multi-label, multi-feature learning, added shape and spatial features, and fused scores both within and across concepts. For video search, it supported both automatic and interactive retrieval.

References

2007

  1. THU and ICRC at TRECVID 2007
    Jinhui Yuan, Zhishan Guo, Li Lv, Wei Wan, Teng Zhang, Dong Wang, Xiaobing Liu, Cailiang Liu, Shengqi Zhu, Duanpeng Wang, Yang Pang, Nan Ding, Ying Liu, Jiangping Wang, Xiujun Zhang, and 15 more authors
    In TREC Video Retrieval Evaluation (TRECVID) Workshop, Nov 2007
    Notebook paper; joint entry of Tsinghua University and Intel China Research Center