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Classification of Tenderness of Large Cooked Beef Joints Using Wavelet and Gabor Textural Features

Classification of Tenderness of Large Cooked Beef Joints Using Wavelet and Gabor Textural Features

Transactions of the ASABE- 49(5): 1447-1454

Thirty-two large cooked beef joints were clustered into tender and tough using k-means clustering based on the shear values obtained from an Instron machine. An image acquisition system was set up to capture images of beef samples, and four different groups of texture features, i.e., wavelet features (WF), Gabor features (GF), wavelet Gabor features (WGF), and a combination of wavelet features and Gabor features (CWG), were extracted from the captured images. After removing the collinear redundancy using principal component analysis (PCA) among the various features in each group, a linear discrimination function was employed for classifying the tenderness of the samples. The discrimination results, together with the error rates estimated by the posterior probability error rate estimate method, suggest that the WGF provided the best classification results, followed by WF and CWG. The GF performed the worst among the four methods. Some existing problems with the wavelet transform and Gabor filter, which limited the accuracy of the proposed method, were also discussed.

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Accession: 015283978

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