can we measure beauty? computational evaluation of coral reef aesthetics

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ID: 231345
2015
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Abstract
The natural beauty of coral reefs attracts millions of tourists worldwide resulting in substantial revenues for the adjoining economies. Although their visual appearance is a pivotal factor attracting humans to coral reefs current monitoring protocols exclusively target biogeochemical parameters, neglecting changes in their aesthetic appearance. Here we introduce a standardized computational approach to assess coral reef environments based on 109 visual features designed to evaluate the aesthetic appearance of art. The main feature groups include color intensity and diversity of the image, relative size, color, and distribution of discernable objects within the image, and texture. Specific coral reef aesthetic values combining all 109 features were calibrated against an established biogeochemical assessment (NCEAS) using machine learning algorithms. These values were generated for ∼2,100 random photographic images collected from 9 coral reef locations exposed to varying levels of anthropogenic influence across 2 ocean systems. Aesthetic values proved accurate predictors of the NCEAS scores (root mean square error < 5 for N ≥ 3) and significantly correlated to microbial abundance at each site. This shows that mathematical approaches designed to assess the aesthetic appearance of photographic images can be used as an inexpensive monitoring tool for coral reef ecosystems. It further suggests that human perception of aesthetics is not purely subjective but influenced by inherent reactions towards measurable visual cues. By quantifying aesthetic features of coral reef systems this method provides a cost efficient monitoring tool that targets one of the most important socioeconomic values of coral reefs directly tied to revenue for its local population.
Reference Key
haas2015peerjcan Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Andreas F. Haas;Marine Guibert;Anja Foerschner;Tim Co;Sandi Calhoun;Emma George;Mark Hatay;Elizabeth Dinsdale;Stuart A. Sandin;Jennifer E. Smith;Mark J.A. Vermeij;Ben Felts;Phillip Dustan;Peter Salamon;Forest Rohwer
Journal pediatrics
Year 2015
DOI
10.7717/peerj.1390
URL
https://peerj.com/articles/1390/
Keywords
Machine learning coral reef image analysis aesthetics

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