Automated Microalgae Image Classification

In this paper we present a new method for automated recognition of 12 microalgae that are most commonly found in water resources of Thailand. In order to handle some difficulties encountered in our problem such as unclear algae boundary and noisy background, we proposed a new method for segmenting algae bodies from an image background and proposed a new method for computing texture descriptors from a blurry texture object. Feature combination approach is applied to handle a variation of algae shapes of the same genus. Sequential Minimal Optimization (SMO) is used as a classifier. An experimental result of 97.22% classification accuracy demonstrates an effectiveness of our proposed method