  In this paper we propose an ensemble of local and deep features for object
classification. We also compare and contrast effectiveness of feature
representation capability of various layers of convolutional neural network. We
demonstrate with extensive experiments for object classification that the
representation capability of features from deep networks can be complemented
with information captured from local features. We also find out that features
from various deep convolutional networks encode distinctive characteristic
information. We establish that, as opposed to conventional practice,
intermediate layers of deep networks can augment the classification
capabilities of features obtained from fully connected layers.
