Stage of Decay Estimation Exploiting Exogenous and Endogenous Image Attributes to Minimize Manual Labeling Efforts and Maximize Classification Performance

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.