Graph Cuts-Based Feature Extraction of Plant Leaf

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 279)

Abstract

As leaf is one of the most important organs in a plant, contour features of plant leaves are important for the identification of plant species. So researchers have proposed many methods to improve the progress of the plant identification. In this paper, we present a graph cuts-based method using Min-Cut/Max Flow algorithm to obtain the leaf blade section. Then, five basic features are computed to further obtain six digital morphological features. These experimental results show that the graph cuts algorithm and the presented leaf features are important for leaf recognition.

Keywords

Plant leaf Graph cuts Statistical features Feature extraction 

Notes

Acknowledgment

The work reported in this paper was supported by Jinhua Polytechnic under the research grant 2011S002, and the Talent Start-up Foundation of Zhejiang A&F University under grant No. 2013FR059.

References

  1. 1.
    Fu H, Chi Z (2006) Combined thresholding and neural network approach for vein pattern extraction from leaf images. IEE Proc Vis Image Signal Process 153:881–892CrossRefGoogle Scholar
  2. 2.
    Daly DC, Hickey LJ, Johnson KR, Mitchell JD, Wilf P, Wing SL (2009) Manual of leaf architecture. CABI, AndersonGoogle Scholar
  3. 3.
    Du JX, Wang XF, Zhang GJ (2007) Leaf shape based plant species recognition. Appl Math Comput 185(2):883–893CrossRefMATHGoogle Scholar
  4. 4.
    Ye Y, Chen C, Li CT, Fu H, Chi Z (2004) A computerized plant species recognition system. In: IEEE proceedings of 2004 international symposium on intelligent multimedia, video and speech processing, 2004 pp 723–726Google Scholar
  5. 5.
    Baker B, Olszyk DM, Tingey D (1996) Digital image analysis to estimate leaf area. J Plant Physiol 148:530–535CrossRefGoogle Scholar
  6. 6.
    Chien CF, Lin TT (2002) Leaf area measurement of selected vegetable seedlings using elliptical Hough transform. Trans ASAE 45(5):1669–1677Google Scholar
  7. 7.
    Hiroyoshi I, Hirohisa N, Seishi N (2002) Diallel analysis of leaf shape variations of citrus varieties based on elliptic Fourier descriptors. Breed Sci 52:89–94CrossRefGoogle Scholar
  8. 8.
    Eriksson AP, Barr O, Astrom K (2006) Image segmentation using minimal graph cuts. Published at: Swedish symposium on image. http://www.maths.lth.se/vision/publdb/reports/pdf/eriksson-barretal-ssia-06.pdf
  9. 9.
    Zhou H, Zheng J, Wei L (2013) Texture aware image segmentation using graph cuts and active contours. Pattern Recogn 46(6):1719–1733CrossRefMATHGoogle Scholar
  10. 10.
    Zheng Q, Dong E, Cao Z, Sun W, Li Z (2013) Modified localized graph cuts based active contour model for local segmentation with surrounding nearby clutter and intensity inhomogeneity. Signal Process 93(4):961–966CrossRefGoogle Scholar
  11. 11.
    Yang Y, Han S, Wang T, Tao W, Tai X (2013) Multilayer graph cuts based unsupervised color-texture image segmentation using multivariate mixed student’s t-distribution and regional credibility merging. Pattern Recogn 46(4):1101–1124CrossRefMATHGoogle Scholar
  12. 12.
    Kim D, Paik J (2012) Automatic moving object segmentation using histogram-based graph cut and label maps. Electron Lett 48(19):1198–1199CrossRefGoogle Scholar
  13. 13.
    Dinic EA (1970) Algorithm for solution of a problem of maximum flow in networks with power estimation. Soviet Math Dokl 11:1277–1280Google Scholar
  14. 14.
    Yuri B, Vladimir K (2004) An experimental comparison of min-cut/max-flow Algorithms for energy minimization in vision. IEEE Trans Pattern Anal Mach Intell 26(9):1124–1137CrossRefGoogle Scholar
  15. 15.
    Boykov Y, Gareth FL (2006) Graph cuts and efficient N-D image segmentation. Int J Comput Vision 70(2):109–131CrossRefGoogle Scholar
  16. 16.
    Peng B, Zhang L, Zhang D (2013) A survey of graph theoretical approaches to image segmentation. Pattern Recogn 46(3):1020–1038CrossRefGoogle Scholar
  17. 17.
    Yuri B, Olga V, Ramin Z (2001) Efficient approximate energy minimization via graph Cuts. IEEE Trans PAMI 20(12):1222–1239Google Scholar
  18. 18.
    Vladimir K, Ramin Z (2004) What energy functions can be minimized via graph cuts? IEEE Trans Pattern Anal Mach Intell 26(2):147–159CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Jinhua PolytechnicJinhuaChina
  2. 2.Tianmu CollegeZhejiang A&F UniversityLin’anChina

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