Classifying beef muscle and fat pixels using BP-GA neural network

Genetic Algorithm(GA), which has the potential to search optimal global answer values of problem, was applied to improve the shortage of the original Back-Propagation(BP) algorithm with randomly selecting values in some limitations as its first weights. For the sake of discussing the feasibility of classifying beef muscle and fat pixels with BP neural network, a 3-12-1 three-layer BP-GA neural network was built, and three experiments on classifying the beef muscle and fat pixels were implemented with it. The input vector of the BP neural network is the RGB values of each pixel, and the training and test set samples are 62 and 43, respectively. The final results of the three experiments are as follows: the recognition rates of training samples are 100%, 100%, 98.3871%, respectively, and the recognition rates of test samples are 97.6744%, 97.6744%, 100%, respectively. The experimental results show that, although the recognition rates of the training sample sets and the test sample sets are all over 97%, due to the RGB values of beef image pixels being dispersive in color space and their characteristics available for clustering being inconspicuous, whether it can use BP neural network to classify beef muscle and fat pixels or not needs to study furthermore, such as constructing a rather reasonable network topology, or selecting rather appropriate training sample sets.