Multi Feature Fusion at Score-Level for Appearance-based Person Re-Identification
This MATLAB-Code provides algorithms for score-level fusion with application to person re-identification.
This includes eight methods for score normalization
- Product of likelihood-ratios
- Logistic regression
- Decimal scaling
- Doble sigmoid
and eight methods for feature weighting
- Equal weighting
- EER-based (equal error rate)
- Based on Rank 1 performance
- Based on Rank 10 performance
- nAUC-based (normalized area under cumulative matching characteristic (CMC) curve)
- NCW (non-confidence width)
- PROPER (pairwise optimization of projected genuine-impostor overlap)
The framework provides interfaces for evaluation on the frequently used person re-identification benchmark datasets
The framework also contains implementations of publicly available features for appearance-based person re-identification
- Texture features: Local Binary Pattern (LBP), Maximum Response (MR8)
- Color features: Black Value Tint (BVT) histogram, Lightness Color Opponent (Lab) histogram, weighted Hue Saturation Value (wHSV) histogram, Maximum Stable Color Regions (MSCR)
- Combinations: Ensemble of Localized Features (ELF), Salient Dense Correspondence (SDC)
Additionally, the framework provides interfaces to combine score-level fusion with feature-level fusion (metric learning with KISSME or kernel-LFDA).
If you consider using the code provided on this page, please reference the following:
Eisenbach, M., Kolarow, A., Vorndran, A., Niebling, J., Gross, H.-M.
Evaluation of Multi Feature Fusion at Score-Level for Appearance-based Person Re-Identification
in:Proc. of Int. Joint Conf. on Neural Networks (IJCNN 2015), Killarney, Ireland, pp. 469-476, IEEE 2015
Please contact firstname.lastname@example.org for requests.