LAUSR

The research of paleontology is an essential part of contemporary earth science. However, the time-consuming manual identification process has always been cumbrous in the field of paleontology. Since conventional algorithms have limited efficiency in processing images of complicated paleontological fossils. In this work, a combinational machine learning method, which comprises appropriate image preprocessing, Scale-invariant feature transform (SIFT), K-means clustering (K-means), and Support Vector Machine (SVM) are applied to realize automatic recognition of paleontological images under microscope. It is demonstrated that this combined algorithm has superior performance in morphological feature extraction in the case of complex mineral textures. With this technique, the overall average accuracy of image recognition is 84.5%, which significantly improved the efficiency of sample analysis in the field of paleontology.