Color Image Segmentation of Live Grouper Fish with Complex Background in Seawater

Abstract:

The color live fish image segmentation is a important procedure of the understanding fish behavior. We have introduced an simple segmentation method of live Grouper Fish color images with seawater background and presented a segmentation framework to extract the whole fish image from the complex background of seawater. Firstly, we took true color pictures of live Grouper fish in seawater using waterproof camera and save these pictures files as RGB format files, called True-color Images. Secondly, we extracted R,G and B planes of a true color Grouper fish image, painted and compared their histograms of R,G and B planes. Thirdly, we segmented these RGB images and the R,G and B planes of a true color Grouper fish image with the k-means clustering algorithm, using the kmeans () function which is packaged by the Clustering Analysis ToolBox of Matlab 2012(a). Finally, we analyzed the relationships between these histograms and segmented images, and then got a conclusion is that : using the B plane of these RGB images as Input-matrix to do clustering segmentation algorithm by the kmeans () function of Matlab Clustering ToolBox, can got a fulfilling segmentation results.

• Periodical: Applied Mechanics and Materials (Volume 743)
• Pages: 293-302
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.743.293
• Online since: March 2015
• Authors: G.Q. Ma, Y.C. Tian, X.L. Li, K.Z. Xing, Su Xu
• Keywords: Fish Image, Image Segmentation, K-Means Clustering

References

[1] Sushmita Mitra, Partha Pratim Kundu. Satellite image segmentation with Shadowed C-Means. Information Sciences 181 (2011) 3601–3613.

DOI: 10.1016/j.ins.2011.04.027

[2] Hong Yaoa, Qingling Duana, Daoliang Li, et al. An improved K-means clustering algorithm for fish image segmentation. Mathematical and Computer Modelling, Volume 58, Issues 3–4, August 2013, Pages 790–798.

DOI: 10.1016/j.mcm.2012.12.025

[3] Sinmund Clausen, Katharina Greiner, Odd Anderson, et al. Automatic Segmentation of Overlapping Fish Using Shape Priors, 15th Scandinavian Conference, SCIA 2007, Aalborg, Denmark, June 10-24, 2007. pp: 11-20.

[4] Peizhe Zhang, Canping Li. Region-based color image segmentation of fishes with complex background in water, 2011 IEEE International Conference on Computer Science and Automation Engineering, Shanghai, China, 10-12 June 2011. pp: 596-600.

DOI: 10.1109/csae.2011.5953291

[5] M. Chambah, D. Semani et al. Underwater Color Constancy: Enhancement of Automatic Live Fish Recognition, http: /hal. archives-ouvertes. fr/docs/00/26/37/34/PDF/manuscrit279. pdf.

DOI: 10.1117/12.524540