Abstract
In this research work, the fractional fuzzy two-dimensional linear discriminant analysis (FF2DLDA) an extension of fuzzy 2DLDA (F2DLDA) is proposed and applied to grade the two dimensional pomegranate fruit images. The research work investigates three existing and one novel mathematical feature extraction simulation technique to address the problem of non-destructive pomegranate fruit grading and classification. The approach is to use four feature extraction based mathematical simulation techniques, includes traditional 2DLDA, Fractional 2DLDA (FLDA), Fuzzy 2DLDA and FF2DLDA. The proposed technique holds the most discriminative features, by redefining the fuzzy between class scatter matrix of F2DLDA as fractional fuzzy between-class scatter matrix. In order to classify the extracted features, the kernel support vector machine (KSVM) along with all 2DLDA variants are used. The results show that FF2DLDA is manifold superior to the existing techniques, since the fractional fuzzy between class scatter matrix assigns a small fuzzy weight for edge classes and large fuzzy weight for non-edge classes. This effectively weakens the effect of edge class selection problem which is present in traditional 2D feature extraction techniques.
Published Version
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