  Commercial detection in news broadcast videos involves judicious selection of
meaningful audio-visual feature combinations and efficient classifiers. And,
this problem becomes much simpler if these combinations can be learned from the
data. To this end, we propose an Multiple Kernel Learning based method for
boosting successful kernel functions while ignoring the irrelevant ones. We
adopt a intermediate fusion approach where, a SVM is trained with a weighted
linear combination of different kernel functions instead of single kernel
function. Each kernel function is characterized by a feature set and kernel
type. We identify the feature sub-space locations of the prediction success of
a particular classifier trained only with particular kernel function. We
propose to estimate a weighing function using support vector regression (with
RBF kernel) for each kernel function which has high values (near 1.0) where the
classifier learned on kernel function succeeded and lower values (nearly 0.0)
otherwise. Second contribution of this work is TV News Commercials Dataset of
150 Hours of News videos. Classifier trained with our proposed scheme has
outperformed the baseline methods on 6 of 8 benchmark dataset and our own TV
commercials dataset.
