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Data Preprocessing Technique for Neural Networks Based on Image Represented by a Fuzzy Function
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DOI：10.1109/TFUZZ.2019.2911494.png)
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摘要
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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 he 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 mast 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.
Keyword:
Data preprocessing
image classification
image fuzzification
image represented by fuzzy function (IRFF)
neural network (NN)
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11.9
论文数:
5.0K
被引数:
2.9W
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