TRECVID 2005 represented the fifth running of a TREC-style video retrieval evaluation, the goal of which remained to promote progress in content-based retrieval from digital video via open, metrics-based evaluation. Over time this effort is yielding a better understanding of how systems can effectively accomplish such retrieval and how one can reliably benchmark their performance. TRECVID is funded by the Disruptive Technology Office (DTO) and the National Institute of Standards and Technology (NIST). Forty-two teams from various research organizations — 11 from Asia/Australia, 17 from Europe, 13 from the Americas, and 1 US/EU team — participated in one or more of five tasks: shot boundary determination, low-level feature (camera motion) extraction, high-level feature extraction, search (automatic, manual, interactive) or pre-production video management. Results for the first four tasks were scored by NIST using manually created truth data for shot boundary determination and camera motion detection. Feature and search submissions were evalu-
[1]
A. W. Kemp,et al.
Randomization, Bootstrap and Monte Carlo Methods in Biology
,
1997
.
[2]
Sara Shatford,et al.
Analyzing the Subject of a Picture: A Theoretical Approach
,
1986
.
[3]
Wei-Ying Ma,et al.
Image and Video Retrieval
,
2003,
Lecture Notes in Computer Science.
[4]
A. I.,et al.
Neural Field Continuum Limits and the Structure–Function Partitioning of Cognitive–Emotional Brain Networks
,
2023,
Biology.
[5]
B. Manly.
Randomization, Bootstrap and Monte Carlo Methods in Biology
,
2018
.
[6]
Sanjeev R. Kulkarni,et al.
Rapid estimation of camera motion from compressed video with application to video annotation
,
2000,
IEEE Trans. Circuits Syst. Video Technol..
[7]
Bernd Freisleben,et al.
Estimation of arbitrary camera motion in MPEG videos
,
2004,
Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..
[8]
Peter G. B. Enser,et al.
Retrieval of Archival Moving Imagery - CBIR Outside the Frame?
,
2002,
CIVR.