Abstract
Botanists and foresters empirically determine plant categories mainly via visual features of leaves, e.g. leaf shape, leaf margin, leaf arrangement and leaf venation. The leaf shape and leaf margin can be captured easily with cheap devices. As a result, automatic plant recognition is generally based on leaf shape or margin features. In this paper, a set of features that depict leaf shape and margin are proposed to improve the performance of plant recognition. The proposed margin features utilize the area ratio to quantify the convexity/concavity of each contour point at different scales and such margin features are effective in capturing the global information and contour details. The area ratio is the ration of the disk to the inside of the contour. The proposed shape features use a combination of morphological features to characterize the global shape of the leaf, which has merits in preserving the geometric properties of leaf shape. Additionally, a series of multi-grained fusion methods that combine the margin feature and global shape feature are proposed as a better representation of a leaf. To validate the effectiveness and generalization, we evaluate our methods on two public datasets: Swedish Leaf dataset and ICL Leaf dataset. The experimental results show the superiority of our methods over state-of-the-art shape methods.
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Notes
Other baselines use average recognition rate as metric.
The average recognition rate of 20 trials.
References
Alajlan N, El Rube I, Kamel M S, Freeman G (2007) Shape retrieval using triangle-area representation and dynamic space warping. Pattern Recognit 40 (7):1911–1920
Belhumeur P N, Chen D, Feiner S, Jacobs D W, Kress W J, Ling H, Lopez I, Ramamoorthi R, Sheorey S, White S et al (2008) Searching the world’s herbaria: a system for visual identification of plant species. In: European conference on computer vision. Springer, pp 116–129
Belongie S, Malik J, Puzicha J (2002) Shape matching and object recognition using shape contexts. IEEE Trans Pattern Anal Mach Intell (4):509–522
Chaki J, Parekh R, Bhattacharya S (2015) Plant leaf recognition using texture and shape features with neural classifiers. Pattern Recognit Lett 58:61–68
Chang C C, Lin C J (2011) Libsvm: a library for support vector machines. ACM Trans Intell Syst Technol (TIST) 2(3):27
Cover T, Hart P (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13(1):21–27
Cui C, Fang H, Deng X, Nie X, Dai H, Yin Y (2017) Distribution-oriented aesthetics assessment for image search. In: Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval. ACM, pp 1013–1016
Du J X, Wang X F, Zhang G J (2007) Leaf shape based plant species recognition. Appl Math Comput 185(2):883–893
Felzenszwalb P F, Schwartz JD (2007) Hierarchical matching of deformable shapes. In: IEEE conference on computer vision and pattern recognition, 2007. CVPR’07. IEEE, pp 1–8
Hu R X, Jia W, Ling H, Huang D (2012) Multiscale distance matrix for fast plant leaf recognition. IEEE Trans Image Process 21(11):4667–4672
Kadir A, Nugroho L E, Susanto A, Santosa P I (2013) Leaf classification using shape, color, and texture features. arXiv:14014447
Kalyoncu C, Toygar Ö (2015) Geometric leaf classification. Comput Vis Image Underst 133:102–109
Knight D, Painter J, Potter M (2010) Plant leaf classification for a mobile field guide
Kumar N, Belhumeur P N, Biswas A, Jacobs D W, Kress W J, Lopez I C, Soares J V (2012) Leafsnap: a computer vision system for automatic plant species identification. In: Computer vision–ECCV 2012. Springer, pp 502–516
Lee S H, Chan C S, Wilkin P, Remagnino P (2015) Deep-plant: plant identification with convolutional neural networks. In: IEEE international conference on image processing (ICIP), vol 2015. IEEE, pp 452–456
Level Otsu N (1979) A threshold selection method from gray-level histogram. IEEE Trans Syst Man Cybern 9(1):62–66
Ling H, Jacobs D W (2007) Shape classification using the inner-distance. IEEE Trans Pattern Anal Mach Intell 29(2):286–299
Pradhan A K, Mohanty P, Behera S (2014) A real time based physiological classifier for leaf recognition. Int J Adv Comput Res 4(1):337
Priya C A, Balasaravanan T, Thanamani A S (2012) An efficient leaf recognition algorithm for plant classification using support vector machine. In: 2012 international conference on pattern recognition, informatics and medical engineering (PRIME). IEEE, pp 428–432
Rojanamontien M, Watchareeruetai U (2017) Shape recognition by using scale invariant feature transform for contour. In: 2017 14th international joint conference on computer science and software engineering (JCSSE). IEEE, pp 1–6
Satti V, Satya A, Sharma S (2013) An automatic leaf recognition system for plant identification using machine vision technology. Int J Eng Sci Technol 5(4):874
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:14091556
Söderkvist O (2001) Computer vision classification of leaves from swedish trees. Master’s Thesis, Linköping University
Tzionas P, Papadakis S E, Manolakis D (2005) Plant leaves classification based on morphological features and a fuzzy surface selection technique. In: Fifth international conference on technology and automation, Thessaloniki, pp 365–370
Wang J, Bai X, You X, Liu W, Latecki L J (2012) Shape matching and classification using height functions. Pattern Recognit Lett 33(2):134–143
Wijesingha D, Marikar F (2012) Automatic detection system for the identification of plants using herbarium specimen images. Trop Agric Res 55(23):833–839
Wu J, Rehg J M (2011) Centrist: a visual descriptor for scene categorization. IEEE Trans Pattern Anal Mach Intell 33(8):1489–1501
Wu S G, Bao F S, Xu E Y, Wang Y X, Chang Y F, Xiang Q L (2007) A leaf recognition algorithm for plant classification using probabilistic neural network. In: 2007 IEEE international symposium on signal processing and information technology. IEEE, pp 11–16
Xu C, Liu J, Tang X (2009) 2d shape matching by contour flexibility. IEEE Trans Pattern Anal Mach Intell 31(1):180–186
Yang X, Koknar-Tezel S, Latecki LJ (2009) Locally constrained diffusion process on locally densified distance spaces with applications to shape retrieval. In: IEEE conference on computer vision and pattern recognition, 2009. CVPR 2009. IEEE, pp 357–364
Yang C, Wei H, Yu Q (2016) Multiscale triangular centroid distance for shape-based plant leaf recognition. In: ECAI, pp 269–276
Zhang T (2001) An introduction to support vector machines and other kernel-based learning methods. AI Mag 22(2):103
Zhang D, Lu G (2004) Review of shape representation and description techniques. Pattern Recognit 37(1):1–19
Acknowledgements
This work was supported by National Key R&D Program of China under grant no. 2018YFB0204100, National Nature Science Foundation of China under grant no. 61772419, Changjiang Scholars and Innovative Research Team in University under grant no. IRT_17R87.
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Zhang, X., Zhao, W., Luo, H. et al. Plant recognition via leaf shape and margin features. Multimed Tools Appl 78, 27463–27489 (2019). https://doi.org/10.1007/s11042-019-07846-0
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DOI: https://doi.org/10.1007/s11042-019-07846-0