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Intell."],"published-print":{"date-parts":[[2017,12]]},"abstract":"<jats:p> In this paper, we propose a novel approach of Gabor feature based on bi-directional two-dimensional principal component analysis ((2D)<jats:sup>2<\/jats:sup>PCA) for somatic cells recognition. Firstly, Gabor features of different orientations and scales are extracted by the convolution of Gabor filter bank. Secondly, dimensionality reduction of the feature space applies (2D)<jats:sup>2<\/jats:sup>PCA in both row and column. Finally, the classifier uses Support Vector Machine (SVM) to achieve our goal. The experimental results are obtained using a large set of images from different sources. 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