Automated Identification of Camellia Shells and Seeds using a Combination of HOG and LBP Features
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Abstract:
A self-built camellia fruit shell and seed sorting device has achieved rapid sorting of camellia fruit shells and seeds using a HOG-LBP feature fusion method developed for the automated identification of camellia fruit shells and seeds. The device collected shell and seed images, segmented the foreground images of shells and seeds by pre-processing the collected images, and extracted the HOG shape features and LBP texture features of shells and seeds. To improve the detection rate, the dimensionality of HOG features, LBP features, and HOG+LBP features were reduced using principal component analysis (PCA), and the shell and seed classifier was trained using four machine learning algorithms, namely, BP neural network, naive Bayesian model, K-nearest neighbor, and support vector machine. The performance of the trained classifiers were compared with the results from the test set. The experimental results showed that the HOG+LBP features based classifier trained by support vector machine performed best in identification of shells and seeds, with a training accuracy of 100% and an average test accuracy of 96.00%. Moreover, the identification accuracies after feature fusion were all higher than those based on single features, indicating that the proposed method is effective for the automated identification of camellia fruit shells and seeds.
