  Autonomous detection and classification of objects are admired area of
research in many industrial applications. Though, humans can distinguish
objects with high multi-granular similarities very easily; but for the
machines, it is a very challenging task. The convolution neural networks (CNN)
have illustrated efficient performance in multi-level representations of
objects for classification. Conventionally, the existing deep learning models
utilize the transformed features generated by the rearmost layer for training
and testing. However, it is evident that this does not work well with
multi-granular data, especially, in presence of deceptive similar classes
(almost similar but different classes). The objective of the present research
is to address the challenge of classification of deceptively similar
multi-granular objects with an ensemble approach thfat utilizes activations
from multiple layers of CNN (deep features). These multi-layer activations are
further utilized to build multiple deep decision trees (known as Random forest)
for classification of objects with similar appearance. The Fruits-360 dataset
is utilized for evaluation of the proposed approach. With extensive trials it
was observed that the proposed model outperformed over the conventional deep
learning approaches.

Computer Vision and Pattern Recognition