Monday, May 07, 2007
MPEG-7 Shape Classification Results
15 training shapes per class, 5 testing shapes. Classification rates were about as expected, perhaps slightly better than expected, since correct classification means that the correct model must have the highest likelihood out of all 70 shape models. In other words, there is no difference between 2nd best and worst in terms of classification error.
Figure 1. Classification rate (averaged over all shape classes) vs. number of principle components retained in the tangent space PCA shape models in each class. There was an error in the classification with 15 PCs (perhaps due to singularities). On average, overfitting does not appear to be a major problem in the models since the classification rate increases monotonically with model dimensionality.
Figure 2. Average classification rates for each shape class (averaged over all numbers of principle components).
Figure 3. Max. classification rates for each shape class (maximized over all numbers of principle components).
Figure 1. Classification rate (averaged over all shape classes) vs. number of principle components retained in the tangent space PCA shape models in each class. There was an error in the classification with 15 PCs (perhaps due to singularities). On average, overfitting does not appear to be a major problem in the models since the classification rate increases monotonically with model dimensionality.
Figure 2. Average classification rates for each shape class (averaged over all numbers of principle components).
Figure 3. Max. classification rates for each shape class (maximized over all numbers of principle components).
# posted by Jared @ 6:36 PM 
