Abstract
To accurately estimate the beef marbling standard (BMS) number of live cattles 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 extracted from imagings. 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 results confirmed that the estimation accuracy of BMS number by ICA was higher than that by PCA.
