Establishing the stage of decay for human remains is an important and common task in forensic anthropology, which is presently done manually by trained experts. We aim to develop a supervised vision system, leveraging a large human decomposition image dataset, to perform stage of decay estimation. To minimize the manual labeling efforts and costs, we introduce and evaluate a label propagation method that exploits both exogenous and endogenous image attributes in conjunction with domain knowledge. The proposed label propagation method performed up to twice as fast (100%) as the baseline method and improved the classification macro-averaged F1 score of the best model from 0.79 to 0.863 (9.24%) when using three stage of decay classes and from 0.695 to 0.803 (15.54%) when using four stage of decay classes.
(Publisher abstract provided.)