Texture and Color Distribution-based Classification for Live Coral Detection

The endangered Acropora cervicornis coral has been considered a bellwether for coral reef habitat change. Given the recent discovery of a concentrated region of growth off Belize, our group is studying automatic abundance estimation of live A. cervicornis with the goal of future rapid assessment monitoring. In this paper we present a novel technique for the automatic segmentation of coral image sets. While others have had limited success applying machine learning techniques on color or texture-based features, our project presents several confounding factors in acquisition and in image content for which we must compensate. Our technique uses color features called quantile functions and SIFT texture features, and classifies local image regions as either live A. cervicornis or other image content using linear Support Vector Machines. We present promising results on a series of images for which we have manual segmentations to train with or test against. We also compare our results to those achieved using established raw color features. Our approach may not only greatly reduce the time cost of future abundance estimates of A. cervicornis, but also may be generalized to other coral vision problems.