The week's pick
Reseach Article
A Combined Color, Texture and Edge Features Based Approach for Identification and Classification of Indian Medicinal Plants
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 6 - Number 12 |
| Year of Publication: 2010 |
| Authors: Basavaraj S. Anami, Suvarna S. Nandyal, A. Govardhan |
10.5120/1122-1471
|
Basavaraj S. Anami, Suvarna S. Nandyal, A. Govardhan . A Combined Color, Texture and Edge Features Based Approach for Identification and Classification of Indian Medicinal Plants. International Journal of Computer Applications. 6, 12 ( September 2010), 45-51. DOI=10.5120/1122-1471
Abstract
This paper presents a method for identification and classification of images of medicinal plants such as herbs, shrubs and trees based on color and texture feature using SVM and neural network classifier. The tribal people in India classify plants according to their medicinal values. In the system of medicine called Ayurveda, identification of medicinal plants is considered an important activity in the preparation of herbal medicines. Ayurveda medicines have become alternate for allopathic medicine. Hence, leveraging technology in automatic identification and classification of medicinal plants has become essential. Plant species belonging to different classes such as Papaya, Neem, Tulasi, Aloe and Garlic are considered in this work. This paper presents edge and color descriptors that have low-dimension, effective and simple. In addition, the rotation invariant texture descriptors namely, directional difference and the gradient histogram are used. These features are obtained from 900 images of medicinal plants and used to train and test the image samples of three classes with SVM and radial basis exact fit neural network (RBENN). The classification accuracies for color, edge texture features are 74% and 80% respectively. The accuracy is improved to 90% with combined color and texture features. The results are encouraging for tree image plants than herbs and shrubs due to distinguishing feature of stem.
References
- A. Commander Sunil Tyagi, (2008). A Comparative Study of SVM Classifiers and Artificial Neural Networks Application for Rolling Element Bearing Fault Diagnosis using Wavelet Transform Preprocessing, World Academy of Science, Engineering and Technology , Vol. 43,pp 309-317.
- B.S.Manjunath,Jens-Rainer Ohm, Vinod V. Vasudevan,and Akio Yamada,(2001). Color and texture descriptors,IEEE Transactions On Circuits And Systems For Video Technology, Vol. 11, No. 6, pp. 703-715.
- Carol L. Novak and Steven A Safar,(1992). Anatomy of a color histogram, proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 92),pp. 599-605.
- D Shi, L Xu, L Han,(2008). Image retrieval using both color and texture features, The Journal of China Universities of Posts and Telecommunications, Vol 14, pp.94-99.
- Dong Kwon Park, Yoon Seok Jeon, Chee Sun Won,(2000). Efficient Use of Local Edge Histogram Descriptor, International Multimedia Conference Proceedings of ACM workshops on Multimedia, pp 51-54.
- Dong-cheng Shi, Lan Xu and Ling-yan Han,(2007). Image retrieval using both color and texture feature , The Journal of China Universities of Posts and Telecommunications, Vol.14. pp 94-99.
- Dong-cheng Shi, Lan Xu and Ling-yan Han,(2008). Image retrieval using both color and edge histogram, Electronic Imaging and Multimedia Technology, Vol. 6833(2), pp.6833361-6833367.
- Justin Domke and Yiannis Aloimonos,(2006).Deformation and Viewpoint Invariant Color Histograms, Proceedings of British Machine Vision Conference(BMVC),Edinburg UK, pp.267-270.
- Kamarul Hawari Ghazali, Mohd Marzuki Mustafa, Aini Hussain,(2007).Color image processing of weed classification:A comparision of two Feature Extraction Techniques,Proceedings of the International Conference on Electrical Engineering and Informatics, pp 607-610.
- K. Singh, M. Ma, and D.W. Park,(2003). Histogram Approach for Content-based Image Retrieval, Proceedings in Visualization, Imaging, and Image Processing.
- L. Cinque, S. Levialdi, A. Pellicanò, K.A. Olsen,(1999). Color-Based Image Retrieval Using Spatial-Chromatic Histograms, IEEE International Conference on Multimedia Computing and Systems (ICMCS'99) - Volume 2, pp. 969.
- Matti Pietik.inen, Topi M.enp.. and Jaakko Viertola,(2002). Color texture classification with color histograms and local binary patterns, In Workshop on Texture Analysis in Machine Vision, pp. 109-112
- Shamik Sural, Gang Qian and Sakti Pramanik,(2002). Segmentation and histogram generation using the HSV color space for image retrieval, International Conference on Image Processing(ICIP), Vol. 2 , pp. 589-592.
- Shengsheng Yu,Chaobing Huang,Jingli Zhou,(2006). Color Image Retrieval Based On Color-Texture-Edge Feature Histogram, International Journal of Image and Graphics ,Vol 6 No 4 pp 583-598.
- Xiao-Feng, Wang, De-Shuang, Huang, Ji-Xiang, Dua,HuanXu, LaurentHutte,(2008). Classification of plant leaves with complicated background, Applied Mathematics and Computation, Vol.205 pp.916–926.
- Yining Deng, B. S. Manjunath, Charles Kenney, Michael S. Moore, and Hyundoo Shin, (2001).An Efficient Color Representation for Image Retrieval, IEEE transactions on image processing, Vol. 10, No. 1,pp140-147.
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