  This paper presents a novel approach to exploit the distinctive invariant
features in convolutional neural network. The proposed CNN model uses Scale
Invariant Feature Transform (SIFT) descriptor instead of the max-pooling layer.
Max-pooling layer discards the pose, i.e., translational and rotational
relationship between the low-level features, and hence unable to capture the
spatial hierarchies between low and high level features. The SIFT descriptor
layer captures the orientation and the spatial relationship of the features
extracted by convolutional layer. The proposed SIFT Descriptor CNN therefore
combines the feature extraction capabilities of CNN model and rotation
invariance of SIFT descriptor. Experimental results on the MNIST and
fashionMNIST datasets indicates reasonable improvements over conventional
methods available in literature.
