Parham, J. | bioRxiv Science

bioRxiv2022

Comparison of Two Individual Identification Algorithms for Snow Leopards (Panthera uncia) after Automated Detection

1. Photo-identification of individual snow leopards (Panthera uncia) is the primary technique for density estimation for the species. A high volume of images from multiple projects, combined with pre-existing historical catalogs, has made identifying snow leopard individuals within the images cost- and time-intensive. 2. To speed the classification among a high volume of photographs, we trained and evaluated image classification methods for PIE v2 (a triplet loss network), and we compared PIEs accuracy to the HotSpotter algorithm (a SIFT based algorithm). Analyzed data were collected from a curated catalog of free-ranging snow leopards photographed across years (2012-2019) in Afghanistan and from samples in captivity provided by zoos from Finland, Sweden, Germany, and the United States. 3. Results show that PIE outperforms HotSpotter. We also found weaknesses in the initial PIE model, like a minor amount of background matching, which was addressed, although likely not fully resolved, by applying background subtraction (BGS) and left-right mirroring (LR) methods. The PIE BGS LR model combined with Hotspotter showed a Rank-1: 85%, Rank-5: 95%, Rank-20: 99%. 4. Overall, our results recommend implementing PIE v2 simultaneously with HotSpotter on Whiskerbook.org.

bioinformatics↗

bioRxiv2021

Comparison of Three Individual Identification Algorithms for Sperm Whales (Physeter macrocephalus) after Automated Detection

Photo-identification of individual sperm whales (Physeter macrocephalus) is the primary technique for mark-recapture-based population analyses for the species The visual appearance of the fluke -- with its distinct nicks and notches -- often serves as the primary visual differentiator, allowing humans to make recorded sightings of specific individuals. However, the advent of digital photography and the significant increase in volume of images from multiple projects in combination with pre-existing historical catalogs has made applying the method more challenging.with the required human labor for de-duplication (reduction of Type II errors) and reconciliation of sightings between large datasets too cost- and time-prohibitive. To address this, we trained and evaluated the accuracy of PIE v2 (a triplet loss network) along with two existing fluke trailing edge-matching algorithms, CurvRank v2 and Dynamic Time Warping (DTW), as a mean to speed comparison among a high volume of photographs. Analyzed data were collected from a curated catalog of well-known sperm whales sighted across years (2005-2018) off the island of Dominica. The newly-trained PIE model outperformed the older CurvRank and DTW algorithms, and PIE provided the following top-k individual ID matching accuracy on a standard min-3/max-10 sighting training data set: Rank-1: 87.0%, Rank-5: 90.5%, and Rank-12: 92.5%. An essential aspect of PIE is that it can learn new individuals without network retraining, which can be immediately applied in the presence of (and for the resolution of) duplicate individuals in overlapping catalogs. Overall, our results recommend the use of PIE v2 and CurvRank v2 for ID reconciliation in combination due to their complementary performance.

ecology↗