No abstract

Humans can recognize objects well even if they only show the shape of objects or an object is composed of several components. But, most of the classifiers in the deep learning framework are trained through original images without removing complex elements inside the object. And also, they do not remove things other than the object to be classified. So the classifiers are not as effective as the human classification of objects because they are trained with the original image which has many objects that the classifier does not want to classify. In this respect, we found out which pre-processing can improve the performance of the classifier the most by comparing the results of using data through other pre-processing. In this paper, we try to limit the amount of information in the object to a minimum. To restrict the information, we use anisotropic diffusion and isotropic diffusion, which are used for removing the noise in the images. By using the anisotropic diffusion and the isotropic diffusion for the pre-processing, only shapes of objects were passed to the classifier. With these diffusion processes, we can get similar classification accuracy compared to when using the original image, and we found out that although the original images are diffused too much, the classifier can classify the objects centered on discriminative parts of the objects.