Multi features combination for automated zooplankton classification

Abstract

Zooplankton are the key components of marine food webs. The abundance of it influences the ocean ecological balance. To efficiently monitor species richness of zooplankton and protect marine environment, marine biologists and computer vision experts started to research automated zooplankton classification system with computer vision technologies. Most current research focuses on achieving high classification accuracy. In this paper, we propose a new system based on multi features combination to enhance the zooplankton classification performance. In our system, the geometric and grayscale features, Local Binary Patterns features, and Inner-distance Shape Context features are extracted as low-level features. According to the properties of machine learning algorithms, an appropriate algorithm is chosen to generate middle-level features by processing all kinds of low-level features. After that, we concatenate middle-level features and apply Support Vector Machine to get the final classifier. By combining different types of features, the system we proposed can capture richer biomorphic information than those with a few features. And the experimental results also show that our system achieves better classification performance.