article · IEEE Journal of Oceanic Engineering · 2006

Binary Plankton Image Classification

Xiaoou Tang, Feng Lin, Scott Samson, Andrew Remsen

Cited by 39Abstract and details only

Abstract

In marine biology study, it is important to investigate the distribution of plankton organisms. Because of the overwhelming data size, automatic processing of the large amount of image data collected by underwater image recorders becomes inevitable. However, due to the fragmentation and the large within-class variations of binary plankton images, it is difficult to extract reliable shape features. In this paper, we propose several new shape descriptors and use a normalized multilevel dominant eigenvector estimation method to select a best feature set for binary plankton image classification. We achieve more than 91% classification accuracy in experiments on more than 3000 images

Cite this paper

APA 7

Tang, X., Lin, F., Samson, S., & Remsen, A. (2006). Binary Plankton Image Classification. IEEE Journal of Oceanic Engineering, 31(3), 728–735. https://doi.org/10.1109/joe.2004.836995

MLA 9

Tang, Xiaoou, et al. “Binary Plankton Image Classification.” IEEE Journal of Oceanic Engineering, vol. 31, no. 3, 2006, pp. 728–35, https://doi.org/10.1109/joe.2004.836995.

Chicago

Tang, Xiaoou, Feng Lin, Scott Samson, and Andrew Remsen. 2006. “Binary Plankton Image Classification.” IEEE Journal of Oceanic Engineering 31 (3): 728–35. https://doi.org/10.1109/joe.2004.836995.

Harvard

Tang, X. et al. (2006) ‘Binary Plankton Image Classification’, IEEE Journal of Oceanic Engineering, 31(3), pp. 728–735. Available at: https://doi.org/10.1109/joe.2004.836995.

BibTeX

@article{tang2006binary,
  title = {{Binary Plankton Image Classification}},
  author = {Tang, Xiaoou and Lin, Feng and Samson, Scott and Remsen, Andrew},
  journal = {IEEE Journal of Oceanic Engineering},
  volume = {31},
  number = {3},
  pages = {728--735},
  year = {2006},
  doi = {10.1109/joe.2004.836995}
}

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