This paper addresses issues in the retention and analysis of large image collections by examining issues with a forensic unlabeled dataset of over 1M human decomposition photos.
In many domains, large image collections are key ways in which information about relevant phenomena is retained and analyzed, yet it remains challenging to use such data in research and practice. The authors’ aim is to investigate this problem in the context of a forensic unlabeled dataset of over 1M human decomposition photos. To make this collection usable by experts, various body parts first need to be identified and traced through their evolution despite their distinct appearances at different stages of decay from "fresh" to "skeletonized". The authors developed an unsupervised technique for clustering images that builds sequences of similar images representing the evolution of each body part through stages of decomposition. Evaluation of the authors’ method on 34,476 human decomposition images shows that their method significantly outperforms the state-of-the-art clustering method in this application. (Published abstract provided)
Similar Publications
- Enhanced DNA Profiling of the Semen Donor in Late Reported Sexual Assaults: Use of Y-Chromosome-Targeted Pre-amplification and Next Generation Y-STR Amplification Systems
- Real-Time Sample-Mining and Data-Mining Approaches for the Discovery of Novel Psychoactive Substances (NPS)
- A Systematic Study of Liquid Chromatography to Separate Eighteen Natural Cannabinoids for Potency Testing of Hemp-Based Products Using Diode Array Detector and Electrospray Ionization Mass Spectrometry