DeepFish: Accurate underwater live fish recognition with a deep architecture

@article{Qin2016DeepFishAU,
  title={DeepFish: Accurate underwater live fish recognition with a deep architecture},
  author={Hongwei Qin and Xiu Li and Jian Liang and YiGang Peng and Changshui Zhang},
  journal={Neurocomputing},
  year={2016},
  volume={187},
  pages={49-58}
}
Underwater object recognition is in great demand, while the research is far from enough. The unrestricted natural environment makes it a challenging task. We propose a framework to recognize fish from videos captured by underwater cameras deployed in the ocean observation network. First, we extract the foreground via sparse and low-rank matrix decomposition. Then, a deep architecture is used to extract features of the foreground fish images. In this architecture, principal component analysis… CONTINUE READING
Highly Cited
This paper has 23 citations. REVIEW CITATIONS

Citations

Publications citing this paper.
Showing 1-10 of 10 extracted citations

MomentsNet: A simple learning-free method for binary image recognition

2017 IEEE International Conference on Image Processing (ICIP) • 2017

RGB-D object recognition based on RGBD-PCANet learning

2017 IEEE International Conference on Mechatronics and Automation (ICMA) • 2017

References

Publications referenced by this paper.
Showing 1-10 of 40 references

Distinctive Image Features from Scale-Invariant Keypoints

International Journal of Computer Vision • 2004
View 4 Excerpts
Highly Influenced

LIBSVM: A library for support vector machines

ACM TIST • 2011
View 3 Excerpts
Highly Influenced

The pyramid match kernel: discriminative classification with sets of image features

Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 • 2005
View 3 Excerpts
Highly Influenced

DeepFace: Closing the Gap to Human-Level Performance in Face Verification

2014 IEEE Conference on Computer Vision and Pattern Recognition • 2014
View 1 Excerpt

Similar Papers

Loading similar papers…