Classification of radiolarian images with hand-crafted and deep features

• Published in: Computers & geosciences Vol. 109; pp. 67 - 74
• Main Authors: Keçeli, Ali Seydi, Kaya, Aydın, Keçeli, Seda Uzunçimen
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-12-2017
• Subjects: Deep learning; Image features; Pattern classification; Radiolarian image; Deep learning; Radiolarian image; Image features; Pattern classification
• ISSN: 0098-3004; 1873-7803
• DOI: 10.1016/j.cageo.2017.08.011

Radiolarians are planktonic protozoa and are important biostratigraphic and paleoenvironmental indicators for paleogeographic reconstructions. Radiolarian paleontology still remains as a low cost and the one of the most convenient way to obtain dating of deep ocean sediments. Traditional methods for identifying radiolarians are time-consuming and cannot scale to the granularity or scope necessary for large-scale studies. Automated image classification will allow making these analyses promptly. In this study, a method for automatic radiolarian image classification is proposed on Scanning Electron Microscope (SEM) images of radiolarians to ease species identification of fossilized radiolarians. The proposed method uses both hand-crafted features like invariant moments, wavelet moments, Gabor features, basic morphological features and deep features obtained from a pre-trained Convolutional Neural Network (CNN). Feature selection is applied over deep features to reduce high dimensionality. Classification outcomes are analyzed to compare hand-crafted features, deep features, and their combinations. Results show that the deep features obtained from a pre-trained CNN are more discriminative comparing to hand-crafted ones. Additionally, feature selection utilizes to the computational cost of classification algorithms and have no negative effect on classification accuracy.
•A classification method for radiolarian images is proposed.•A radiolarian image dataset is prepared.•Combinations of different features are compared with different base classifiers.•The results show that deep features are more successful than hand-crafted features.•The feature selection over deep features has a positive effect on computation time.