Classification of lamb carcass using machine vision: Comparison of statistical and neural network analyses

Abstract

In this study, the ability of artificial neural network (ANN) models to predict the lamb carcass grades using features extracted from lamb chop images was compared with multivariate statistical model (discriminant function analysis (DFA)) with respect to the classification accuracy. Twelve geometric features were extracted from each of the acquired lamb chop images. In addition, 136 texture features (90 co-occurrence, 10 run length and 36 grey-level difference histogram) were also extracted from the acquired images. Four sets of reduced features comprising six geometric, eight co-occurrence texture, four run length texture and four grey-level difference histogram features were generated based on the results of dimensionality reduction. The four sets of reduced features, individually and in different combinations, were utilised for classification using ANN and DFA. Several network configurations were tested and the classification accuracy of 96.9% was achieved from the three-layer multi-layer perceptron (MLP) network. Its performance was 12% better than that from the DFA. Geometric features play a very important role in classification. Co-occurrence features also play an equally important role in classification.