Graph Synthesis for Pig Breed Classification From Muzzle Images

Abstract

Non-intrusive and automated detection of pig breeds, particularly from visual standpoint, is important from a food quality tracking perspective. In this work, colour as well as texture based visual descriptors from muzzle images have been identified, which, serve as breed-identifiers to separate four common pig-breeds: Duroc, Ghungroo, Hampshire and Yorkshire. While these handcrafted visual descriptors by themselves are fairly robust and discriminative, it is recognized that by controlling the decision space by choosing the feature-type based on colour or texture or both and the order in which particular breeds are siphoned, classification accuracy can be improved considerably. In that light, a stable, relatively data-independent, breed-specific, hierarchical tree synthesis and feature selection procedure is proposed based on a breed-pair cluster separation table. The proposed approach has been compared with the state of the art Phylogenetic distance based Hierarchical Agglomerative Clustering algorithm (AGNES) and also with the standard decision tree classification algorithm. On cross-validation, When completely different sets of pigs were used for training and testing (50-50 split), the proposed algorithm reported relatively high mean classification accuracies of 86.45% for Duroc, 93.02% for Ghungroo, 86.91% for Hampshire and 98.54% for Yorkshire, respectively.