Virus Image Classification using PHOG and SGLDM Texture Features

Authors

  • Archana  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

Keywords:

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

Abstract

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.

References

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2017-08-31

Issue

Volume 2, Issue 4, July-August-2017

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Archana, Syeda Asra, " Virus Image Classification using PHOG and SGLDM Texture Features, IInternational 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.