Estimation of Marbling Score in Live Cattle Based on ICA and a Neural Network

To accurately estimate the Beef Marbling Standard(BMS) number of live cattle using ultrasound echo imagings, we have developed a image recognition method by use of a neural network. This paper examines the efficiency of applying Independent Component Analysis(ICA) to the compression of multidimensional image features. ICA can accurately separate a target signal because of its independence assumption, while Principal Component Analysis(PCA), a conventional method, involves decorrelation of the components. We have implemented the estimation tests by use of ultrasound echo imagings of 103 live cattles. Multidimentional texture features extracted from the imagings were compressed by ICA, and then the estimation of BMS number was conducted by using a neural network. The estimation accuracy was evaluated based on the cross validation method. We caluculated the correlation coefficient between the actual and estimated values using 100 different data sets. The results confirmed that the correlation coefficient between the actual and the estimated values was higher by ICA (R = 0.70, p <; 0.01) than by PCA (R = 0.62, p <; 0.01). Also, we conducted the comparison experiments between the ICA based estimation and the estimation by an experienced inspector. The both methods examined the same ultrasound images. Even the experienced inspector failed to estimate BMS number because the estimation requires highly professional skill. The correlation coefficient between the actual and the estimated values was R = 0.70 (p <; 0.01). As a result, we confirmed that the proposed method had much the same capability as the experienced inspector to estimate BMS number.