Enhanced Image Classification With Data Augmentation Using Position Coordinates
This addresses the problem of image classification accuracy and robustness for computer vision applications, but it is incremental as it builds on existing data augmentation methods.
The paper tackles image classification by incorporating pixel position coordinates into neural networks, resulting in improved accuracy on MNIST and SVHN datasets and resolution-invariant performance.
In this paper we propose the use of image pixel position coordinate system to improve image classification accuracy in various applications. Specifically, we hypothesize that the use of pixel coordinates will lead to (a) Resolution invariant performance. Here, by resolution we mean the spacing between the pixels rather than the size of the image matrix. (b) Overall improvement in classification accuracy in comparison with network models trained without local pixel coordinates. This is due to position coordinates enabling the network to learn relationship between parts of objects, mimicking the human vision system. We demonstrate our hypothesis using empirical results and intuitive explanations of the feature maps learnt by deep neural networks. Specifically, our approach showed improvements in MNIST digit classification and beats state of the results on the SVHN database. We also show that the performance of our networks is unaffected despite training the same using blurred images of the MNIST database and predicting on the high resolution database.