Volume 16, Issue 1 (5-2019)
JSDP 2019, 16(1): 111-124 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Mostajer Kheirkhah F, Asghari H, Yazdani D. On the use of Textural Features and Neural Networks for Leaf Recognition. JSDP 2019; 16 (1) :111-124
URL: http://jsdp.rcisp.ac.ir/article-1-792-en.html
URL: http://jsdp.rcisp.ac.ir/article-1-792-en.html
ICT Research Institute, ACECR
Abstract: (3911 Views)
for recognizing various types of plants, so automatic image recognition algorithms can extract to classify plant species and apply these features. Fast and accurate recognition of plants can have a significant impact on biodiversity management and increasing the effectiveness of the studies in this regard.
These automatic methods have involved the development of recognition techniques and digital image processing pattern. Most of the previous studies on the classification and identification of plant species from leaf images are based on the shape, texture and color features. There were also different methods of data modeling which have been used to leave plant recognition.
In this paper, we investigate a novel approach for the recognition of plant species using texture feature GIST to extract general features. In the classification step, Patternnet feed forward neural network algorithm has been applied. Essentially, the GIST feature has been designed to be employed for image classification. In this study, GIST feature vectors are considered as the basis of the leaves’ classification. The GIST descriptor of an image is computed by the first filtering of an image by a filter bank of Gabor filters, and then averaging the responses of filters in each block on a no overlapping grid.
For evaluation of our approach, we have applied the algorithm on scan and pseudo-scan images of two famous different datasets Image CLEF2012 and Leaf snap with a high various. The results show that in comparison to some widely used algorithms, our approach outperforms in the case of time and also the accuracy of classification. Substantial results can be achieved when the image of the plants are aligned with one another and when we deal with pseudo scan images.
The detection of combinations of leaves that have jagged edges is an important contribution of this study. In many of the previous algorithms, the computational complexity of this detection is high. While by using the GIST feature vector, these types of images are processed simply and precisely (above 90%).
precisely (above 90%).
These automatic methods have involved the development of recognition techniques and digital image processing pattern. Most of the previous studies on the classification and identification of plant species from leaf images are based on the shape, texture and color features. There were also different methods of data modeling which have been used to leave plant recognition.
In this paper, we investigate a novel approach for the recognition of plant species using texture feature GIST to extract general features. In the classification step, Patternnet feed forward neural network algorithm has been applied. Essentially, the GIST feature has been designed to be employed for image classification. In this study, GIST feature vectors are considered as the basis of the leaves’ classification. The GIST descriptor of an image is computed by the first filtering of an image by a filter bank of Gabor filters, and then averaging the responses of filters in each block on a no overlapping grid.
For evaluation of our approach, we have applied the algorithm on scan and pseudo-scan images of two famous different datasets Image CLEF2012 and Leaf snap with a high various. The results show that in comparison to some widely used algorithms, our approach outperforms in the case of time and also the accuracy of classification. Substantial results can be achieved when the image of the plants are aligned with one another and when we deal with pseudo scan images.
The detection of combinations of leaves that have jagged edges is an important contribution of this study. In many of the previous algorithms, the computational complexity of this detection is high. While by using the GIST feature vector, these types of images are processed simply and precisely (above 90%).
precisely (above 90%).
Type of Study: Research |
Subject:
Paper
Received: 2017/12/2 | Accepted: 2019/01/9 | Published: 2019/06/10 | ePublished: 2019/06/10
Received: 2017/12/2 | Accepted: 2019/01/9 | Published: 2019/06/10 | ePublished: 2019/06/10
References
1. [1] S. L. Pimm, C. N. Jenkins, R. Abell, T. M. Brooks, J. L. Gittleman, L.N. Joppa, P.H. Raven, C.M. Roberts, and J.O. Sexton, "The biodiversity of species and their rates of extinction, distribution, and protection," Science, vol. 344(6187), 2014. [DOI:10.1126/science.1246752] [PMID]
2. [2] A. Joly, H. Müller, H. Goëau, H. Glotin, C. Spampinato, A. Rauber, P. Bonnet, W. P. Vellinga, R. B. Fisher, and R. Planquè, "LifeCLEF: Multimedia life species identifica-tion," In EMR@ ICMR, pp. 7-13, April 2014. [DOI:10.1007/978-3-319-11382-1_20]
3. [3] A. R. Backes, D. Casanova, and O. M. Bruno, "A complex network-based approach for boundary shape analysis," Pattern Recognition, vol. 42(1), pp. 54-67, 2009. [DOI:10.1016/j.patcog.2008.07.006]
4. [4] C. Caballero and M. C. Aranda, "Plant species identification using leaf image retrieval," In Proceedings of the ACM International Con-ference on Image and Video Retrieval, July 2010. pp. 327-334. [DOI:10.1145/1816041.1816089]
5. [5] N. Kumar, P. N. Belhumeur, A. Biswas, D. W. Jacobs, W. J. Kress, I. C. Lopez, and J. V. Soares, "Leafsnap: A computer vision system for automatic plant species identification." In Computer Vision-ECCV 2012 Springer, Berlin, Heidelberg, pp. 502-516. [DOI:10.1007/978-3-642-33709-3_36]
6. [6] N. Sakai, S. Yonekawa, A. Matsuzaki, and H. Morishima, "Two-dimensional image analysis of the shape of rice and its application to separating varieties," Journal of Food Engineering, vol. 27(4), pp. 397-407, 1996. [DOI:10.1016/0260-8774(95)00022-4]
7. [7] J. X. Du, X. F. Wang, and G. J. Zhang, "Leaf shape based plant species recognition," Applied mathematics and computation," vol. 185 (2), pp.883-893, 2007. [DOI:10.1016/j.amc.2006.07.072]
8. [8] Z. Wang, Z. Chi, D. Feng, and Q. Wang, "Leaf image retrieval with shape features," In International Conference on Advances in Visual Information Systems, Springer, Berlin, Heidelberg, 2000. pp. 477-487. [DOI:10.1007/3-540-40053-2_42]
9. [9] T. Beghin, J. S. Cope, P. Remagnino, and S. Barman, "Shape and texture based plant leaf classification," In International Conference on Advanced Concepts for Intelligent Vision Systems Springer, Berlin, Heidelberg, December 2010. pp. 345-353. [DOI:10.1007/978-3-642-17691-3_32]
10. [10] B. S. Bama, S. M. Valli, S. Raju, and V. A. Kumar, "Content based leaf image retrieval (CBLIR) using shape, color and texture features," Indian Journal of Computer Science and Engineering, vol. 2(2), pp.202-211, 2011.
11. [11] H. Kebapci, B. Yanikoglu, and G. Unal, "Plant image retrieval using color, shape and texture features," The Computer Journal, vol. 54(9), pp.1475-1490, 2010. [DOI:10.1093/comjnl/bxq037]
12. [12] S. Abbasi, F. Mokhtarian, and J. Kittler, "Reliable classification of chrysanthemum leaves through curvature scale space," Scale-Space Theory in Computer Vision, pp. 284-295. 1997. [DOI:10.1007/3-540-63167-4_58]
13. [13] Z. Wang, Z. Chi, and D. Feng, "Fuzzy integral for leaf image retrieval," In Fuzzy Systems, Proceedings of the 2002 IEEE International Conference, Vol. 1, 2002. pp. 372-377.
14. [14] J. X. Du, C. M. Zhai, and Q. P. Wang, "Recognition of plant leaf image based on fractal dimension features," Neurocomputing, vol. 116, pp.150-156, 2013. [DOI:10.1016/j.neucom.2012.03.028]
15. [15] L. W. Yang and X. F. Wang, "Leaf image recognition using fourier transform based on ordered sequence," In International Conference on Intelligent Computing, Springer, Berlin, Heidelberg, 2012. pp. 393-400. [DOI:10.1007/978-3-642-31588-6_51]
16. [16] Q. P. Wang, J. X. Du, and C. M. Zhai, "Recognition of leaf image based on ring projection wavelet fractal feature," In Advanced Intelligent Computing Theories and Appli-cations. With Aspects of Artificial Intelligence, Springer, Berlin, Heidelberg, 2010. pp. 240-246. [DOI:10.1007/978-3-642-14932-0_30]
17. [17] S. Prasad, P. Kumar, and R. C. Tripathi, "Plant leaf species identification using curvelet transform," In IEEE Computer and Communi-cation Technology (ICCCT), 2011 2nd Inter-national Conference, September 2011. pp. 646-652. [DOI:10.1109/ICCCT.2011.6075212]
18. [18] A. Kadir, L. E. Nugroho, A. Susanto, and P. I. Santosa, "Experiments of Zernike moments for leaf identification," Journal of Theoretical and Applied Information Technology (JATIT), vol. 41(1), pp.82-93, 2012.
19. [19] J. Pan and Y. He, "Recognition of plants by leaves digital image and neural network," In IEEE Computer Science and Software Engineering, 2008 International Conference on Vol. 4, December 2008. pp. 906-910. [DOI:10.1109/CSSE.2008.918]
20. [20] 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 pro-babilistic neural network," In Signal Processing and Information Technology, 2007 IEEE Inter-national Symposium, December 2007. pp. 11-16.
21. [21] C. A. Priya, T. Balasaravanan, and A. S. Thanamani, "An efficient leaf recognition algorithm for plant classification using support vector machine," In Pattern Recognition, Informatics and Medical Engineering (PRIME), 2012 International Conference on IEEE, March 2012. pp. 428-432. [DOI:10.1109/ICPRIME.2012.6208384]
22. [22] N. Valliammal and S. N. Geethalakshmi, "A novel approach for plant leaf image segmentation using fuzzy clustering," Inter-national Journal of Computer Applications, vol. 44(13), pp. 10-20, 2012. [DOI:10.5120/6322-8669]
23. [23] H. Goëau, A. Joly, S. Selmi, P. Bonnet, E. Mouysset, L. Joyeux, J. F. Molino, P. Birnbaum, D. Bathelemy, and N. Boujemaa, "Visual-based plant species identification from crowdsourced data," In Proceedings of the 19th ACM inter-national conference on Multimedia, November 2011. pp. 813-814. [DOI:10.1145/2072298.2072472]
24. [24] J. Wu and J. M. Rehg, "CENTRIST: A visual descriptor for scene categorization," IEEE transactions on pattern analysis and machine intelligence, vol. 33(8), pp. 1489-1501, 2011. [DOI:10.1109/TPAMI.2010.224] [PMID]
25. [25] C. Li, A. Kowdle, A. Saxena, and T. Chen, "Towards holistic scene understanding: Feedback enabled cascaded classification models," In Advances in Neural Information Processing Systems, 2010. pp. 1351-1359.
26. [26] Z. Li and L. Itti, "Saliency and gist features for target detection in satellite images," IEEE Transactions on Image Processing, vol. 20(7), pp. 2017-2029, 2011. [DOI:10.1109/TIP.2010.2099128] [PMID]
27. [27] A. C. Murillo, G. Singh, J. Kosecka, and J. J. Guerrero, "Localization in urban environments using a panoramic gist descriptor," IEEE Trans-actions on Robotics, vol. 29(1), pp. 146-160, 2013. [DOI:10.1109/TRO.2012.2220211]
28. [28] A. Farhadi, M. Hejrati, M. A. Sadeghi, P. Young, C. Rashtchian, J. Hockenmaier, and D. Forsyth, "Every picture tells a story: Generating sentences from images," In European con-ference on computer vision, Springer, Berlin, Heidelberg. Sep 2010. pp. 15-29. [DOI:10.1007/978-3-642-15561-1_2]
29. [29] R. Bharath, "Computer-Assisted Algorithms for Ultrasound Imaging Systems," Doctoral dissertationIndian Institute of Technology Hyderabad. 2018.
30. [30] F. Alaei, A. Alaei, U. Pal, and M. Blumenstein, "Evaluation of Gist Operator for Document Image Retrieval," In 2018 13th IAPR Inter-national Workshop on Document Analysis Systems (DAS) Apr 2018. pp. 369-374. [DOI:10.1109/DAS.2018.43]
31. [31] H. Yalcin, and S. Razavi, "Plant classification using convolutional neural networks," In Agro-Geoinformatics (Agro-Geoinformatics), 2016 Fifth Inter-national Conference, Jul 2016. pp. 1-5. [DOI:10.1109/Agro-Geoinformatics.2016.7577698] [PMCID]
32. [32] A. Caglayan, O. Guclu, and A. B. Can, "A plant recognition approach using shape and color features in leaf images," In International Conference on Image Analysis and Processing, Springer, Berlin, Heidelberg. September 2013. pp. 161-170. [DOI:10.1007/978-3-642-41184-7_17]
33. [33] E. J. Pauwels, P. M. de Zeeuw, and E. B. Ranguelova, "Computer-assisted tree taxonomy by automated image recognition," Engineering Applications of Artificial Intelligence, vol. 22(1), pp. 26-31, 2009. [DOI:10.1016/j.engappai.2008.04.017]
34. [34] J. Chaki, R. Parekh, and S. Bhattacharya, "Plant leaf recognition using texture and shape features with neural classifiers," Pattern Recognition Letters, vol. 58, pp. 61-68, 2015. [DOI:10.1016/j.patrec.2015.02.010]
35. [35] M. A. J. Ghasab, S. Khamis, F. Mohammad, and H. J. Fariman, "Feature decision-making ant colony optimization system for an automated recognition of plant species," Expert Systems with Applications, vol. 42(5), pp. 2361-2370, 2015. [DOI:10.1016/j.eswa.2014.11.011]
36. [36] Z. Zulkifli, P. Saad, and I. A. Mohtar, "Plant leaf identification using moment invariants & general regression neural network," In Hybrid Intelligent Systems (HIS), 2011 11th Inter-national Conference, December 2011. pp. 430-435. [DOI:10.1109/HIS.2011.6122144]
37. [37] A. H. Kulkarni, , H. M. Rai, , K. A. Jahagirdar, and P. S. Upparamani, "A leaf recognition technique for plant classification using RBPNN and Zernike moments," International Journal of Advanced Research in Computer and Communi-cation Engineering, vol. 2(1), pp.984-988, 2013.
38. [38] R. X. Hu, W. Jia, , H. Ling, and D. Huang, "Multiscale distance matrix for fast plant leaf recognition," IEEE Trans.Image Processing, vol. 21(11), pp.4667-4672, 2012. [DOI:10.1109/TIP.2012.2207391] [PMID]
39. [39] D. G. Tsolakidis, D. I. Kosmopoulos, and G. Papadourakis, "Plant leaf recognition using Zernike moments and histogram of oriented gradients," In Hellenic Conference on Artificial Intelligence, 2014, pp. 406-417. [DOI:10.1007/978-3-319-07064-3_33]
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
