Virus Image Classification using PHOG and SGLDM Texture Features

Authors(2) :-Archana, Syeda Asra

Plant virus classification is one of the emerging application areas of image processing. The design of plant classification module must need to recognize the disease, continue with this research some image processing designers focused on designing the such module which classify the virus which are responsible particular plant disease. In this paper we briefly explain the designed system which efficiently classifies the real time virus present at the given input image. The given is processed, based on the collected features the virus present at the given is classified by using ANN classifiers. The module is trained with five different set of virus, the system design and its performance is briefly explained in below section.

Authors and Affiliations

Department of Computer Science and Engineering, Appa Institute of Engineering and Technology Kalaburagi, Karanataka, India
Syeda Asra
Associate Professor, Department of Computer Science and Engineering, Appa Institute of Engineering and Technology Kalaburagi, Karanataka, India

Contrast Limited Adaptive Histogram Equalization, Adaptive Weiner Filtering, PHOG, SGLDM and Machine Learning Classifier.

  1. Sujeet Varshney and Tarun Dalal, “Plant Disease Prediction using Image Processing Techniques A Review”, International Journal of Computer Science and Mobile Computing, Vol. 5, Issue 5, pp. 394-398, 2016.
  2. Gopinath, S and S. Lalitha, "Plant Diseases Detection By Using Image Processing Techniques", International Journal Of Research And Innovation In Engineering Technology, pp. 1-5, 2017.
  3. Sabrol, H and K. Satish, "Tomato plant disease classification in digital images using classification tree", IEEE, 2016.
  4. Megha S, Niveditha C. R, Sowmyshree N and Vidya K, “Image Processing System for Plant Disease Identification by Using FCM-Clustering Technique”, International Journal of Advance Research, Ideas and Innovations in Technology, 2017.
  5. Rajesh Garg, Bhawna Mittal and Sheetal Garg, “Histogram Equalization Techniques for Image Enhancement”, International Journal of Electronics and Communication Technology, Vol. 2, Issue 1, 2011.
  6. Ms. M. S. Priya and Dr. G. M. Kadhar Nawaz, “MATLAB Based Feature Extraction and Clustering Images Using K-Nearest Neighbor Algorithm”, IJAICT, Vol. 2, Issue 11, 2016.
  7. Seyed Ali Amirshahi, Michael Koch, Joachim Denzler and Christoph Redies, “PHOG Analysis of Self Similarity in Eathetic Images”, the International Society for Optical Engineering, 2012.
  8. Tim Kang, “Using Neural Networks for image Classification”, SSJU, 2015.

Publication Details

Published in : Volume 2 | Issue 4 | July-August 2017
Date of Publication : 2017-08-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 776-780
Manuscript Number : CSEIT1724127
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Archana, Syeda Asra, "Virus Image Classification using PHOG and SGLDM Texture Features", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 4, pp.776-780, July-August-2017.
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