Abstract
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.
Highlights
Through the analysis of palaeobios, researchers can explore the origin, evolution and development of life [1]
DATASET In this paper, palaeobios and rock samples are collected from the School of Earth Science and Technology, Southwest Petroleum University
All the palaeobios and rock slice images were taken under a polarized microscope
Summary
Through the analysis of palaeobios, researchers can explore the origin, evolution and development of life [1]. By studying the biological remains and fossils preserved in the stratum, researchers can determine the ages of strata [4], understand crustal development [5], and infer the climate change [6], mineral sedimentation, and oil gas distribution in the geological history [7]. With the exploration and development of oil and minerals, drilling technology and coring technology [8] have provided the possibility for these large number of buried paleontological objects to be seen. The most commonly used technique in paleontological researches is to use drilled and sampled cores to make thin slices [9], so that tiny palaeobios can show their unique overall characteristics and detailed features under an optical microscope [10].
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