Flower Classification using Different Color Channel
Keywords:
Classification, GLCM, LBP, Color moment, SVM, Neural networkAbstract
In this paper, flowers are classified based on color and texture features where features are extracted from different color channel. Database have been used for experiments is Oxford flower 17 category. There are numbers of steps to get the final classification system. The pre-processing techniques like background elimination, noise removal and segmentation are applied on an original color images. From segmented color image R, G and B color channel is obtained if we use the RGB color model. GLCM (Gray Level Co-occurrence Matrix) method and LBP (Local Binary Pattern) methods are used for texture features extraction from the segmented color image. Color moment is used for color features extraction. A well-known classifier like SVM (Support Vector Machine) and NN (Neural Network) are used for classification. This paper contains comparison of classification results, as which classifier is more accurate for flower classification.
References
Dr.S.M.Mukane, Ms.J.A.Kendule, “Flower Classification Using Neural Network Based Image Processing,” Electronics and Communication Engineering (IOSR-JECE), pp 80-85, 2013.
Basavanna M and S. S. Gornale, “Identification and Classification of Flowers Images using Colour models,” International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE), pp. 1003-1007, 2015
D S Guru, Y. H. Sharath and S. Manjunath, “Texture Features and KNN in Classification of Flower Images,” Recent Trends in Image Processing and Pattern Recognition (RTIPPR), 2010.
F. Siraj, H. M. Ekhsan and A. N. Zulkifli, "Flower image classification modeling using neural network," 2014 International Conference on Computer, Control, Informatics and Its Applications (IC3INA), Bandung, pp.81-86, 2014.
C. Y. Huang, Y. L. Lin and W. H. Hsu, "Augmenting flower recognition by automatically expanding training data from web," Multimedia Signal Processing (MMSP), 2015 IEEE 17th International Workshop on, Xiamen, pp.1-6, 2015.
M. E. Nilsback and A. Zisserman, "A Visual Vocabulary for Flower Classification," 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR`06, pp.1447-1454), 2006.
T. Tiay, P. Benyaphaichit and P. Riyamongkol, "Flower recognition system based on image processing," Student Project Conference (ICT-ISPC), 2014 Third ICT International, Nakhon Pathom, pp.99-102, 2014.
S. W. Hong and L. Choi, "Automatic recognition of flowers through color and edge based contour detection," Image Processing Theory, Tools and Applications (IPTA), 2012 3rd International Conference on, Istanbul, pp.141-146, 2012.
A. Angelova, S. Zhu and Y. Lin, "Image segmentation for large-scale subcategory flower recognition," 2013 IEEE Workshop on Applications of Computer Vision (WACV), Tampa, FL, pp.39-45, 2013.
L. Li and Y. Qiao, "Flower image retrieval with category attributes," 2014 4th IEEE International Conference on Information Science and Technology, Shenzhen, pp.805-808, 2014.
Balvant V. Biradar and Santosh P. Shrikhande, “Flower Detection and Counting Using Morphological and Segmentation Technique,” (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 6 (3), pp.2498-2501, 2015.
H. M. Zawbaa, M. Abbass, S. H. Basha, M. Hazman and A. E. Hassenian, "An automatic flower classification approach using machine learning algorithms," 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI), New Delhi, pp.895-901, 2014.
M. E. Nilsback and A. Zisserman, "Automated Flower Classification over a Large Number of Classes," 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing, Bhubaneswar, pp.722-729, 2008.
Y. Liu, F. Tang, D. Zhou, Y. Meng and W. Dong, "Flower classification via convolutional neural network," 2016 IEEE International Conference on Functional-Structural Plant Growth Modeling, Simulation, Visualization and Applications (FSPMA), Qingdao, pp.110-116, 2016.
W. Qi, X. Liu and J. Zhao, "Flower classification based on local and spatial visual cues," 2012 IEEE International Conference on Computer Science and Automation Engineering (CSAE), Zhangjiajie, pp.670-674, 2012.
F. Siraj, M. A. Salahuddin and S. A. M. Yusof, "Digital Image Classification for Malaysian Blooming Flower," 2010 Second International Conference on Computational Intelligence, Modelling and Simulation, Bali, pp.33-38, 2010.
C. Pornpanomchai, P. Sakunreraratsame, R. Wongsasirinart and N. Youngtavichavhart, "Herb flower recognition system (HFRS)," 2010 International Conference on Electronics and Information Engineering, Kyoto, pp.V1-123-V1-127, 2010.
S. Y. Cho, "Content-based structural recognition for flower image classification," 2012 7th IEEE Conference on Industrial Electronics and Applications (ICIEA), Singapore, pp.541-546, 2012.
Y. Pei and W. Cao, "A method for regional feature extraction of flower images," 2010 International Conference on Intelligent Control and Information Processing, Dalian, pp.384-388, 2010.
A. Balleri, K. Woodbridge, C. J. Baker and M. W. Holderied, "Flower classification by bats: Radar comparisons," in IEEE Aerospace and Electronic Systems Magazine, vol. 24, no. 5, pp. 4-7, May 2009.
S. Puttemans and T. Goedemé, "Visual detection and species classification of orchid flowers," 2015 14th IAPR International Conference on Machine Vision Applications (MVA), Tokyo, pp.505-509, 2015.
J. H. Kim, R. G. Huang, S. H. Jin and K. S. Hong, "Mobile-Based Flower Recognition System," 2009 Third International Symposium on Intelligent Information Technology Application, Nanchang, pp.580-583, 2009.
A. B. Mabrouk, A. Najjar and E. Zagrouba, "Image flower recognition based on a new method for color feature extraction," 2014 International Conference on Computer Vision Theory and Applications (VISAPP), Lisbon, Portugal, pp.201-206, 2014.
Rafael do Espírito Santo, “Principal Component Analysis applied to digital image compression”, Einstein (São Paulo) vol.10 no.2 São Paulo Apr./June 2012.
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