Object Classification using Ensemble of Local and Deep Features
This work addresses object classification for computer vision applications, but it is incremental as it builds on existing feature ensemble methods.
The paper tackles object classification by combining local and deep features, showing that intermediate layers of deep networks can enhance classification performance beyond fully connected layers.
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.