Welcome to the Meta-Recognition project webpage. Here you will find solutions and tools for improving recognition systems including downloadable library (libMR) which is free for non-commercial use.
Here is what you can do with our solutions:
- Attribute Fusion – robust fusion for descriptive visual attributes in a binary SVM context for image retrieval applications. For more information click Attribute Fusion
- EVT Failure prediction – a performance prediction method for recognition algorithms based on the use of the statistical extreme value theory (EVT).
For more information click EVT-MR
- ML Failure prediction – a performance prediction method for recognition algorithms based on the use of machine learning classifiers
For more information click ML-MR
- EVT and ML Fusion – strategies for fusing recognition algorithms based either on statistical extreme value theory or machine learning theory
For more information click EVT-MR, ML-MR, or EVT-FUSION
- OpenSet Recognition and 1-vs-Set Machine – Recognition solutions to deal with the Open Set nature of recognition problems. For more information click Open Set
- Exemplar Codes – combining an exemplar-SVM like approach with EVT normalization to form a secondary feature vector for recognition systems. For more information click ExemplarCodes