Texture Classification based on Edge Descriptor texton Co-occurrence Matrix

Authors(3) :-Rasigiri Venkata Lakshmi, Dr. Patnala S. R. Chandra Murty, Dr. U. Ravi Babu

Texture classification is of great importance for image processing and pattern recognition. It has acknowledged a significant amount of attention over the last few decades as it creates the basis of most pattern recognition methods. The object of texture categorization is to match a query image with a allusion image or cluster such that the query has the same illustration texture as the allusion. In this manuscript, we proposed a new descriptor called EDTU for stone texture classification. The image edge information was extracts from texture images using ED. Independent charge of the skylight size, ED is a tiny 8-bit binary number, so it is suitable for real-time applications. Further, the combination of texton unit and ED called EDTU is proposed. In the present study considered seven statistical features based on EDTU matrix. The efficiency of the projected method is tested on two different texture datasets thereby significantly improving the performance in terms of stone texture classification.

Authors and Affiliations

Rasigiri Venkata Lakshmi
Research Scholar, Department of CSE, ANU, Guntur, Andhra Pradesh, India
Dr. Patnala S. R. Chandra Murty
Ass.Professor, Department of CSE, ANU, Guntur, Andhra Pradesh, India
Dr. U. Ravi Babu
Research Supervisor, Department of CSE, ANU, Guntur, Andhra Pradesh, India

Edge Descriptors, Texture Analysis, Feature Fusion, Texture Matrix.

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Publication Details

Published in : Volume 3 | Issue 5 | May-June 2018
Date of Publication : 2018-06-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 933-940
Manuscript Number : CSEIT1835234
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Rasigiri Venkata Lakshmi, Dr. Patnala S. R. Chandra Murty, Dr. U. Ravi Babu, "Texture Classification based on Edge Descriptor texton Co-occurrence Matrix", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 5, pp.933-940, May-June-2018.
Journal URL : http://ijsrcseit.com/CSEIT1835234

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