@inproceedings { 440,
title = {Automated Annotation of Coral Reef Survey Images },
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2012},
month = {June},
address = {Providence, Rhode Island},
abstract = {With the proliferation of digital cameras and automatic
acquisition systems, scientists can acquire vast numbers of
images for quantitative analysis. However, much image
analysis is conducted manually, which is both time consum-
ing and prone to error. As a result, valuable scientific data
from many domains sit dormant in image libraries awaiting
annotation. This work addresses one such domain: coral
reef coverage estimation. In this setting, the goal, as de-
fined by coral reef ecologists, is to determine the percent-
age of the reef surface covered by rock, sand, algae, and
corals; it is often desirable to resolve these taxa at the
genus level or below. This is challenging since the data ex-
hibit significant within class variation, the borders between
classes are complex, and the viewpoints and image quality
vary. We introduce Moorea Labeled Corals, a large multi-
year dataset with 400,000 expert annotations, to the com-
puter vision community, and argue that this type of ecologi-
cal data provides an excellent opportunity for performance
benchmarking. We also propose a novel algorithm using
texture and color descriptors over multiple scales that out-
performs commonly used techniques from the texture clas-
sification literature. We show that the proposed algorithm
accurately estimates coral coverage across locations and
years, thereby taking a significant step towards reliable au-
tomated coral reef image annotation.
},
author = {Oscar Beijbom and Peter J. Edmunds and David I. Kline and B. Greg Mitchell and David Kriegman}
}
