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This paper presents a novel approach to exploit the distinctive invariantfeatures in convolutional neural network. The proposed CNN model uses ScaleInvariant Feature Transform (SIFT) descriptor instead of the max-pooling layer.Max-pooling layer discards the pose, i.e., translational and rotationalrelationship between the low-level features, and hence unable to capture thespatial hierarchies between low and high level features. The SIFT descriptorlayer captures the orientation and the spatial relationship of the featuresextracted by convolutional layer. The proposed SIFT Descriptor CNN thereforecombines the feature extraction capabilities of CNN model and rotationinvariance of SIFT descriptor. Experimental results on the MNIST andfashionMNIST datasets indicates reasonable improvements over conventionalmethods available in literature.

Exploiting SIFT Descriptor for Rotation Invariant Convolutional Neural Network