Efficient Modern Description Methods by Using SURF Algorithm for Recognition of Plant Species
DOI:
https://doi.org/10.14738/aivp.32.1151Keywords:
Pattern Classification, Machine LearningAbstract
Plants are one of one of most valuable natural resources. There would be no life on earth without plants. They unlike humans and animals manufacture their own food by photosynthesis. In every food chain, the plants occupy the first position and lead the chain as source of food. Environment and climate are largely interlinked with plants. Rainfall, humidity and temperature are influenced by presence of plants. Cutting down plants also imbalance the environment which will indirectly affect human life. Even the economic importance of plants is also quite large to mankind. Plants are great contributors of economy. Many countries rely on agriculture as one of the main source of revenue. Other benefits of plants are significant applications in different fields. Medical and agricultural applications are just some instances of plants application.
Plant recognition can be done by using unique characteristic parts of plants. The used part is leaf. Shapes of leaves are useful to do plant recognition and find the species. Bag of words (BoW) and support vector machine (SVM) methods are applied to recognize and identify plants species. Visual contents of images are used and four steps are performed: (i) image preprocessing, (ii) BoW, (iii) train, (v) test. Three combined methods are used on Flavia dataset. The proposed approach is done by Speed-up robust features (SURF) method and two combined method, HARRIS-SURF and features from accelerated segment test-SURF (FAST-SURF). The accuracy of SURF method is higher than other applied methods. It is 92.28395 %. In addition to visional comparison, some quantitative results are measured and compared.
References
. Allen I. White, The History of the Washington State University College of Pharmacy 1891-1991. 1996. pp. 63-65.
. T. K. C. Im, H. Nishida, Recognizing plant species by leaf shapes-a case study of the acer family. Proceedings of IEEE International Conference on Pattern recognition, 1998. pp. 1171.
. Z. Wang, Z. Chi, and D. Feng, Shape based leaf image retrieval, in Vision. Image and Signal Processing, IEE Proceedings, 2003. 150(1): pp. 34–43.
. J. Du, D. Huang, X. Wang, and X. Gu, Computer-aided plant species identification (CAPSI) based on leaf shape matching technique. Transactions of the Institute of Measurement and Control, 2006. 28 (3): pp. 275-284.
. S. G. Wu, F. S. Bao, E. Y. Xu, Y.-X. Wang, Y.-F. Chang, and Q.-L. Xiang, A leaf recognition algorithm for plant classification using probabilistic neural network. IEEE International Symposium on Signal Processing and Information Technology, IEEE explore library, 2007. pp. 11–16.
. Y. Li, Q. Zhu, Y. Cao, and C. Wang, A leaf vein extraction method based on snakes technique. Proceedings of IEEE International Conference on Neural Networks and Brain, 2005.
. S. Belongie, J. Malik, and J. Puzicha, Shape Matching and Object Recognition Using Shape Contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002. 24(4): pp. 509–522.
. H. Ling and D. W. Jacobs, Shape Classification Using the Inner-distance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007. 29(2): pp. 286–299.
. Herbert Bay, Tinne Tuytelaars and Luc Van Gool, SURF: Speeded Up Robust Features. Computer Vision – ECCV 2006, Lecture Notes in Computer Science, 2006. 3951: pp. 404-417.
. P. M. Panchal, S. R. Panchal, S. K. Shah, A Comparison of SIFT and SURF. International Journal of Innovative Research in Computer and Communication Engineering, 2013. 1(2).
. Lowe, D. G., Object Recognition from Local Scale-Invariant Features. Proc. Of the International Conference on Computer Vision, 1999. pp. 1150—1157.
. G. Csurka, C. Dance, L. Fan, J. Williamowski, and C. Bray. Visual categorization with bags of keypoints, in. ECCV’04 workshop on Statistical Learning in Computer Vision, 2004. pp. 59–74.
. Xin Chen, Xiaohua Hu1, Xiajiong Shen, Spatial Weighting of Bag-of-Visual-Words. Proceedings of the 13th pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, 2009. pp. 867-874.
. Yi Yang, Shawn Newsam, Bag-of-visual-words and spatial extensions for land-use classification. Proceedings of SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2010. pp. 270-279.
. Michael Villamizar, Jorge Scandaliaris, Alberto Sanfeliu, and Juan Andrade-Cetto, Combining Color-based Invariat Gradiet Detector with HoG Descriptors for Robust Image Detection in Scenes under Cast Shadows. IEEE International Conference on Robotics and Automation, 2009. pp. 1997-2002.
. Jun Yang, Yu-gag Jiang, Evaluating bag-of-Visual-words representations in scene classification. Proceedings of the International Workshop on Multimedia Information Retrieval, 2007. pp. 197-206.
. David Picard, Nicolas Thome and Matthieu Cord, An Efficient System for Combinig Complementary Kernels in Complex Visual Categorization Tasks. Proceedings of IEEE International Conference of Image Processing (ICIP), 2010. pp. 3877-3880.
. C.-F. Tsai, Bag-Of-Words Representation in Image Annotation: A Review. International Scholarly Research Network, 2012. pp. 1-19.
. Masoud Fathi Kazerouni, Jens Schlemper, and Klaus-Dieter Kuhnert, Comparison of Modern Description Methods for the Recognition of 32 Plant Species. Signal & Image Processing: An International Journal (SIPIJ), 2015.
. H. Laga, S. Kurtek, A. Srivastava, M. Golzarian, and S. Miklavcic, Ariemannian elastic metric for shape-based plant leaf classification. Digital Image Computing: Techniques and Applications, 2012.
. K. Mikolajczyk, and C. Schmid, Scale and Affine Invariant Interest Point Detectors. International Journal of Computer Vision, 2004. 60(1): pp. 63-86.
. A. Bosch, X. Munoz, and R. Marti, Which is the best way to organize/classify images by content?. Image and Vision Computing, 2007. 25(6): pp. 778–791.
. K. Mikolajczyk, B. Leibe, and B. Schiele, Local features for object class recognition. Proceedings of the 10th IEEE International Conference on Computer Vision (ICCV '05), 2005. pp. 1792–1799.
. T. Tuytelaars and K. Mikolajczyk, Local invariant feature detectors: a survey, Foundations and Trends. Computer Graphics and Vision, 2007. 3(3): pp. 177–280.
. C. Harris, M. Stephens, A Combined Corner and Edge Detector, in Proceedings of the Fourth Alvey Vision Conference, 1988. pp. 147-151.
. E. Rosten, T. Drummond, Machine Learning for High-Speed Corner Detection, European Conference on Computer Vision, 2006. 1: pp. 430–443.
. V. Vapnik, The natural of statistical learning theory, Springer-Verleg, New York, USA, 1995
. S. M. Smith and J. M. Brady, SUSAN — A new approach to low level image processing, International Journal of Computer Vision, 1997. 23(34): pp. 45–78.
