charisma: An R package to perform reproducible color characterization of digital images for biological studies
Schwartz, S. T.; Tsai, W. L. E.; Karan, E. A.; Juhn, M. S.; Shultz, A. J.; McCormack, J. E.; Smith, T. B.; Alfaro, M. E.
Show abstract
O_LIAdvances in digital imaging and software tools have provided increasingly accessible datasets and methods for analyzing color evolution. Despite the variety of computational packages available, most rely on color classification before running analyses. Previous methods to characterize color limit the ability to analyze large-scale image databases and are not always representative of biologically relevant color classes, which decrease the accuracy of downstream analyses. C_LIO_LIHere, we present charisma, an R package designed to characterize the distribution of distinct color classes in images suitable for large-scale studies of biological organisms. Here, we demonstrate the utility of our package through an analysis of color evolution in a sample of diverse and charismatic birds, tanagers, in the subfamily Thraupinae. C_LIO_LIWe show that charisma can quickly and accurately classify every pixel in an image and validate these results using pre-identified, canonical color swatches. We find that charisma color classifications are consistent with those made by color-pattern experts in the field. Applying charisma to tanager color evolution, we find that charisma outputs seamlessly integrate with downstream evolutionary analyses. C_LIO_LIOur results demonstrate that using charisma to manually curate and characterize colors in images provides a standardized, reliable, and reproducible framework for high-throughput color classification. C_LI Anonymized Data/Code for Peer ReviewO_LIAnalytic data/code for this manuscript can be found on the following Open Science Frame-work repo: https://osf.io/cqg59/overview?view_only=29c9786a338e48618fabbab2883937cc C_LIO_LIcharisma package GitHub repo: https://anonymous.4open.science/r/charisma-C87E/ C_LI
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- PicoCam: High-resolution 3D imaging of live animals and preserved specimens 93%
- Thinking like a naturalist: enhancing computer vision of citizen science images by harnessing contextual data 93%
- ML-morph: A fast, accurate and general approach for automated detection and landmarking of biological structures in images 93%
Similar papers in this journal
- A fresh look at an old concept: Home-range estimation in a tidy world 90%
- AlleleShift: An R package to predict and visualize population-level changes in allele frequencies in response to climate change 89%
- Geographic potential of the world largest hornet, Vespa mandarinia Smith (Hymenoptera: Vespidae), worldwide and particularly in North America 89%
Similar papers in this journal
Similar papers in this journal
- Improving the accessibility and transferability of machine learning algorithms for identification of animals in camera trap images: MLWIC2 93%
- Synchrotron-source micro-x-ray computed tomography for examining butterfly eyes 92%
- Comparative analysis of convergent jellyfish eyes reveals extensive differences in expression of vision-related genes 91%
Similar papers in this journal
- Phylogeographic model selection using convolutional neural networks 92%
- Chromosome-scale inference of hybrid speciation and admixture with convolutional neural networks 91%
- A snakemake toolkit for the batch assembly, annotation, and phylogenetic analysis of mitochondrial genomes and ribosomal genes from genome skims of museum collections. 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.