IJSTR

International Journal of Scientific & Technology Research

IJSTR@Facebook IJSTR@Twitter IJSTR@Linkedin
Home About Us Scope Editorial Board Blog/Latest News Contact Us
CALL FOR PAPERS
AUTHORS
DOWNLOADS
CONTACT
QR CODE
IJSTR-QR Code

IJSTR >> Volume 7 - Issue 11, November 2018 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



A Flower Recognition System Based On Image Processing And Neural Networks

[Full Text]

 

AUTHOR(S)

Huthaifa Almogdady, Dr. Saher Manaseer, Dr.Hazem Hiary

 

KEYWORDS

VANET, GPS, location, (RSU).

 

ABSTRACT

Recognition is one of computer vision high level processing, the recognition process is mainly based on classifying object by obtaining and analyzing their main distinguishable features. In this paper and as a benchmark dataset we have used Oxford 102 flowers dataset, as it consists of 8189 flowers images that belong to 102 flower species, each species contains 40 to 251 images that has been gathered using internet searching or directly from photographers. we are introducing a flower recognition system for the Oxford 102 flowers dataset using image processing techniques, combined with Artificial neural networks (ANN), based on our proposed methodology, this paper will be divided into 4 main steps; starting with image enhancement, cropping of images used to modify dataset images to create more suitable dataset for next stage. Then image segmentation introduced to separate the foreground (the flower object) from the background (rest of image) where chan-vese active contour has been used, and for the features extraction, all of color, texture and shape have been used, (HSV color descriptor, Gray Level Co-occurrence Matrix (GLCM) as texture descriptor, and Invariant Moments (IM) as a shape descriptor). Finally; the classification process where Back-Propagation Artificial Neural Network (ANN) used. We have achieved (81.19%) as an accuracy rate.

 

REFERENCES

[1] Zanchettin, C., Bezerra, B. L. D., & Azevedo, W. W. (2012, June). A KNN-SVM hybrid model for cursive handwriting recognition. In Neural Networks (IJCNN), The 2012 International Joint Conference on (pp. 1-8). IEEE.

[2] Conti, V., Militello, C., Vitabile, S., & Sorbello, F. (2010). Complex,“Fingerprint Recognition” Intelligent and Software Intensive Systems (CISIS). In 2010 International Conference (pp. 368-375).

[3] Guocai Liu; Weili Yang; Suyu Zhu; Qiu Huang; Min Liu; Haiyan Wu; Zetian Hu; Zaijie Huang; Yuan Yuan; Ke Liu; Wenlin Huang; Bin Liu; Jinguang Liu; Xuping Zhao; Mao Nie; Bingqiang Hu; Jiutang Zhang; Yi Mo; Biao Zeng; Xiang Peng; Jumei Zhou, "PET/CT image textures for the recognition of tumors and organs at risk for radiotherapy treatment planning," in Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 2013 IEEE , vol., no., pp.1-3

[4] Siraj F, Ekhsan H M and Zulkifli A N (2014), Flower image classification modeling using neural network. In Computer, Control, Informatics and Its Applications (IC3INA), International Conference on (pp. 81-86). IEEE.

[5] Tai-Shan Yan, Yong-Qing Tao and Du-wu Cui(2007), Research on handwritten numeral recognition method based on improved genetic algorithm and neural network. Wavelet Analysis and Pattern Recognition. ICWAPR '07. International Conference on, vol.3, no., pp.1271,1276.

[6] Hsu T H, Lee C H and Chen L H (2011), An interactive flower image recognition system. Multimedia Tools and Applications, 53(1), 53-73.

[7] Saitoh T, Aoki K and Kaneko T (2004), Automatic recognition of blooming flowers. In Pattern Recognition, ICPR. Proceedings of the 17th International Conference on (Vol. 1, pp. 27-30). IEEE.‏

[8] Phyu K H, Kutics A and Nakagawa A (2012), Self-adaptive Feature Extraction Scheme for Mobile Image Retrieval of Flowers. In Signal Image Technology and Internet Based Systems (SITIS), Eighth International Conference on (pp. 366-373). IEEE.

[9] Nilsback M E, and Zisserman A (2007), Delving into the Whorl of Flower Segmentation. In BMVC (pp. 1-10).

[10] Chai Y, Lempitsky V and Zisserman A (2011), Bicos: A bi-level co-segmentation method for image classification. Master thesis

[11] Nilsback M E and Zisserman A (2009), An automatic visual flora: segmentation and classification of flower images (Doctoral dissertation, Oxford University).

[12] Tiay T, Benyaphaichit P, and Riyamongkol P (2014), Flower Recognition System Based on Image Processing. In Student Project Conference (ICT-ISPC), Third ICT International (pp. 99-102). IEEE.

[13] T Chan and L Vese(2001), Active contours without edges. in IEEE transactions on image processing 10(2), pp. 266-277.

[14] Hu M K (1962), Visual pattern recognition by moment invariants. Information Theory, IRE Transactions on, 8(2), 179-187.

[15] Haralick Robert M, Karthikeyan Shanmugam and Its' Hak Dinstein(1973), Textural features for image classification. Systems, Man and Cybernetics, IEEE Transactions on 6 (1973): 610-621.‏

[16] Trevor J.. Hastie, Tibshirani, R. J., & Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction. Springer.

[17] Khan F S, Weijer J, Bagdanov A D and Vanrell M (2011), Portmanteau vocabularies for multi-cue image representation. In Advances in neural information processing systems (pp. 1323-1331).

[18] Kanan C and Cottrell G (2010), Robust classification of objects, faces, and flowers using natural image statistics. In Computer Vision and Pattern Recognition (CVPR), IEEE Conference on (pp. 2472-2479).

[19] Ito S and Kubota S (2010), Object classification using heterogeneous co-occurrence features. In Computer Vision–ECCV 2010 (pp. 701-714). Springer Berlin Heidelberg.

[20] Bi-Cos website, comparison information. http://www.robots.ox.ac.uk/~vgg/data/bicos/