  A transfer learning method for generating features suitable for surgical
tools and phase recognition from the ImageNet classification features [1] is
proposed here. In addition, methods are developed for generating contextual
features and combining them with time series analysis for final classification
using multi-class random forest. The proposed pipeline is tested over the
training and testing datasets of M2CAI16 challenges: tool and phase detection.
Encouraging results are obtained by leave-one-out cross validation evaluation
on the training dataset.
