Super Resolution Based Augmentation for Image Classification on Small Data Set
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Abstract
For image classification to be more accurate, image files with high resolution were to be put up for test. Practically, the set of images obtained for classification might be low resolution files. In this case, it was more difficult to do image classification. The result was that the accuracy rate was low. It was unable to be applied for work. We studied to find a solution to fix this problem. The answer to the problem was to do data augmentation which was consisted of Affine Transformation and Super Resolution. Affine Transformation are all linear transformations which can be represented by a matrix and combined into a single overall including rotation, scaling, translation and shearing where all points in an object are transformed in the same way.
Immage Super Resolution is made to increase the resolution and quality of image. This method has been shown that it outperformed the basic interpolation method. In this research, we studied whether Super Resolution is able to enhance the discriminative features of the image and such transformed image is more amenable to classification process. It can be seen that the methods applied were useful to accurately and precisely identify low resolution image files. Besides, we adopted a transfer learning method which was designed and developed for the task. It has been reused as the starting point for a model on the second task which could help us classify the type of image more accurately and precisely.
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