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

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

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

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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.
Journal URL : http://ijsrcseit.com/CSEIT1724127

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