Classification of radiolarian images with hand-crafted and deep features


KEÇELİ A. S. , KAYA A. , KECELI S. U.

COMPUTERS & GEOSCIENCES, cilt.109, ss.67-74, 2017 (SCI İndekslerine Giren Dergi) identifier

  • Cilt numarası: 109
  • Basım Tarihi: 2017
  • Doi Numarası: 10.1016/j.cageo.2017.08.011
  • Dergi Adı: COMPUTERS & GEOSCIENCES
  • Sayfa Sayıları: ss.67-74

Özet

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.