A pragmatic approach to multi-class classification
This work addresses multi-class classification problems, particularly for gesture recognition, but appears incremental as it builds on existing models like multilayer perceptrons.
The authors tackled multi-class classification by introducing a hierarchical method that adds cascades of classifiers to leverage correlations between predicted classes, demonstrating its effectiveness on a ten-class 3D gesture recognition task.
We present a novel hierarchical approach to multi-class classification which is generic in that it can be applied to different classification models (e.g., support vector machines, perceptrons), and makes no explicit assumptions about the probabilistic structure of the problem as it is usually done in multi-class classification. By adding a cascade of additional classifiers, each of which receives the previous classifier's output in addition to regular input data, the approach harnesses unused information that manifests itself in the form of, e.g., correlations between predicted classes. Using multilayer perceptrons as a classification model, we demonstrate the validity of this approach by testing it on a complex ten-class 3D gesture recognition task.