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A Survey of Analyzing Image Texture Using LBP with k Mean Clustering

Akanxa Mishra1 , Namrata Sharma2

Section:Survey Paper, Product Type: Journal Paper
Volume-4 , Issue-5 , Page no. 172-175, May-2016

Online published on May 31, 2016

Copyright © Akanxa Mishra, Namrata Sharma . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Akanxa Mishra, Namrata Sharma, “A Survey of Analyzing Image Texture Using LBP with k Mean Clustering,” International Journal of Computer Sciences and Engineering, Vol.4, Issue.5, pp.172-175, 2016.

MLA Style Citation: Akanxa Mishra, Namrata Sharma "A Survey of Analyzing Image Texture Using LBP with k Mean Clustering." International Journal of Computer Sciences and Engineering 4.5 (2016): 172-175.

APA Style Citation: Akanxa Mishra, Namrata Sharma, (2016). A Survey of Analyzing Image Texture Using LBP with k Mean Clustering. International Journal of Computer Sciences and Engineering, 4(5), 172-175.

BibTex Style Citation:
@article{Mishra_2016,
author = {Akanxa Mishra, Namrata Sharma},
title = {A Survey of Analyzing Image Texture Using LBP with k Mean Clustering},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {5 2016},
volume = {4},
Issue = {5},
month = {5},
year = {2016},
issn = {2347-2693},
pages = {172-175},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=948},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=948
TI - A Survey of Analyzing Image Texture Using LBP with k Mean Clustering
T2 - International Journal of Computer Sciences and Engineering
AU - Akanxa Mishra, Namrata Sharma
PY - 2016
DA - 2016/05/31
PB - IJCSE, Indore, INDIA
SP - 172-175
IS - 5
VL - 4
SN - 2347-2693
ER -

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Abstract

The main unit of CBIR ( Content based image retrieval ) is an image retrieval technique that used to retrieve from the database the most similar images to the query image. CBIR is convenient , fast and efficient over image search approaches. In online image retrieval, the user can submit a query to the retrieval system to search for greed images. This paper begins a different and powerful image Texture illustration based on local binary pattern texture features. The input image is divided into several image from which the Local binary pattern feature circulation are clipped and concatenated into an enhanced feature vector. The achievement of the proposed method is determined in the image texture recognition problem under The aim of this work is to find the best way for characterize a given texture using a binary pattern based method. Among given features edge and color evolution are perform by various kind of techniques but for texture analysis there are few method are available .The key objective of the proposed work is to obtain and efficient Algorithm for texture analysis. To find out the hidden texture for a particular given image.

Key-Words / Index Term

CBIR , image retrieval, texture analysis, LBP, segmentation methods

References

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