Late Fusion and Calibration for Multimedia Event Detection Using Few Examples (Author's Manuscript)
Abstract
The state-of-the-art in example-based multimedia event detection (MED) rests on heterogeneous classifiers whose scores are typically combined in a late-fusion scheme. Recent studies on this topic have failed to reach a clear consensus as to whether machine learning techniques can outperform rule-based fusion schemes with varying amount of training data. In this paper, we present two parametric approaches to late fusion: a normalization scheme for arithmetic mean fusion (logistic averaging) and a fusion scheme based on logistic regression, and compare them to widely used rule-based fusion schemes. We also describe how logistic regression can be used to calibrate the fused detection scores to predict an optimal threshold given a detection prior and costs on errors. We discuss the advantages and shortcomings of each approach when the amount of positives available for training varies from 10 positives (10Ex) to 100 positives (100Ex). Experiments were run using video data from the NIST TRECVID MED 2013 evaluation and results were reported in terms of a ranking metric: the mean average precision (mAP) and R0, a cost-based metric introduced in TRECVID MED 2013.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jul 14, 2014
- Accession Number
- AD1037724
Entities
People
- Cees G. M. Snoek
- Chen Sun
- Dennis C. Koelma
- Eric Yeh
- Gregory K. Myers
- Julie Wong
- Julien Van Hout
- Ramakant Nevatia
Organizations
- SRI International