Toward a Three-Way Image Classification Model: A Case Study on Corn Grain Images

Image processing techniques are essential in understanding and analyzing information based on pictorial data. The three-way decision is a natural extension of the widely accepted binary-decision model with an added third option. We propose a three-way image classification model in this article. The model integrates techniques of image processing and three-way decision for classification problems. The proposed model is performed in six steps, including HSV and extracted textual local binary pattern (LBP) information, which are: principal component analysis (PCA) feature extraction; decision table creation; classification space development; positive, boundary, and negative sets initialization and optimization; boundary reduction; and test verification. The proposed three-way decision model has been tested with a corn grain dataset under challenges such as (e.g. color component variation, surface pattern variation or so on), that achieved 99 percent performance accuracy.

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