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Hurtik, Petr; Molek, Vojtech; Hula, Jan
IEEE transactions on fuzzy systems, 2020-July, 2020-7-00, Letnik: 28, Številka: 7Journal Article
Although data preprocessing is a universal technique that can be widely used in neural networks (NNs), most research in this area is focused on designing new NN architectures. This paper, we propose a preprocessing technique that enriches the original image data using local intensity information; this technique is motivated by human perception. To encode this information into an image, we introduce a new image structure named image represented by a fuzzy function. When using this structure, a crisp intensity value of each pixel is replaced by a fuzzy set given by a membership function constructed with the usage of extremal values from the particular neighborhood of that pixel. We describe this structure and its properties and propose a way in which it can be used as an input into existing NNs without any modifications. Based on our benchmark consisting of three well-known datasets and five NN architectures, we show that the proposed preprocessing can, in most cases, decrease classification error compared with a baseline and two other preprocessing methods. To support our claim, we have also selected several publicly available projects and tested the impact of the preprocessing with a positive result.
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JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
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