Fish recognition from low-resolution underwater images

The limited underwater observation scenarios pose great challenges to the problem of object recognition from the low-resolution underwater images. This paper proposes a framework to explicitly learn the discriminative features from relatively low resolution images, by resorting to deep learning approaches and super-resolution method. Firstly, the framework tackles the problem of limited discriminative information of low resolution images by a single-image super resolution method. Then state-of-the-art deep learning approaches are employed to learn recognition models for the special underwater fish recognition task. The proposed framework can be effectively implemented for real-time underwater object recognition on autonomous underwater vehicles. To verify the effectiveness of our method, experiments on a public underwater image dataset of fishes are carried out. The results show that our framework achieves promising results for fish recognition on underwater image datasets.

[1]  Gabriel Oliver,et al.  Visual sensing for autonomous underwater exploration and intervention tasks , 2015 .

[2]  Chih-Yuan Yang,et al.  Fast Direct Super-Resolution by Simple Functions , 2013, 2013 IEEE International Conference on Computer Vision.

[3]  Euan S. Harvey,et al.  Counting and measuring fish with baited video techniques - an overview , 2007 .

[4]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[5]  Steven J. Cooke,et al.  Action Cameras: Bringing Aquatic and Fisheries Research into View , 2015 .

[6]  Jenq-Neng Hwang,et al.  Tracking Live Fish From Low-Contrast and Low-Frame-Rate Stereo Videos , 2015, IEEE Transactions on Circuits and Systems for Video Technology.

[7]  Robert B. Fisher,et al.  A research tool for long-term and continuous analysis of fish assemblage in coral-reefs using underwater camera footage , 2014, Ecol. Informatics.

[8]  Robert B. Fisher,et al.  Automatic fish classification for underwater species behavior understanding , 2010, ARTEMIS '10.

[9]  Robert B. Fisher,et al.  Detecting, Tracking and Counting Fish in Low Quality Unconstrained Underwater Videos , 2008, VISAPP.

[10]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[11]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[12]  Qiang Chen,et al.  Network In Network , 2013, ICLR.

[13]  Jenq-Neng Hwang,et al.  Automatic fish segmentation via double local thresholding for trawl-based underwater camera systems , 2011, 2011 18th IEEE International Conference on Image Processing.

[14]  Dominique Pelletier,et al.  Underwater video techniques for observing coastal marine biodiversity: A review of sixty years of publications (1952–2012) , 2014 .

[15]  Robert B. Fisher,et al.  Hierarchical classification with reject option for live fish recognition , 2014, Machine Vision and Applications.

[16]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[17]  Hyun Myung,et al.  Vision-based object detection and tracking for autonomous navigation of underwater robots , 2012 .

[18]  Jiwen Lu,et al.  PCANet: A Simple Deep Learning Baseline for Image Classification? , 2014, IEEE Transactions on Image Processing.

[19]  Pascal Vincent,et al.  Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.