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ICSES Transactions on Image Processing and Pattern Recognition
Vol. 4, No. 4, Dec. 2018

A Novel Graph-based Image Mining Technique Using Weighted Substructure

Book Chapter of Image Segmentation: A Guide to Image Mining
Vikramsingh Parihar a,mail_outline, Roshani Nage b, Atul Dahane c
a PRMCEAM, Amravati, India
b SGBAU Amravati, Amravati, India
c SGBAU Amravati, Amravati, India


Highlights and Novelties
1- The number of images to be retrieved can be specified by the user and hence provide a better governing.

2- The first image from the retrieved images is the perfectly matched image for the given query image. The retrieved images are arranged from left to right in the decreasing order of their proximities.

3- All the images are closely matched with the query image. Hence, in case there is any outlier image, it will have some similar features with the query image.

4- A novel approach of mining images using weighted graph substructures is presented.


Manuscript Abstract
This paper presents a novel image mining approach based on weighted substructure. The problem is modeled in terms of creating a dataset of images and extracting the features of each image. Then graphs are generated for each image based on these features. There are many possible ways to obtain features of images from a graph but one of the most natural ways is to represent a graph is by a set of its substructure. The weight factor is used to measure the actual importance of each different substructure in a given graph dataset. On the basis of weighted substructure graphs the image mining process is done. For image mining, an external query image is provided by user. Its features are extracted and graph is generated. Later the substructure of query image is matched with the substructure of the dataset. The most closely matched substructures of images from the dataset are identified and it can be concluded that the identified images are close to the query image. The experiments are carried out on a dataset of 1000 natural as well as synthetic images from online resources and it is found that the mined images are most closely related to the query image.


 Graph Theory   Image Analysis   Image Mining   Image Processing   Preprocessing   Weighted Substructure 


Copyright and Licence
Copyright © International Computer Science and Engineering Society (ICSES). This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution Non Commercial 4.0 International (CC BY-NC 4.0) license, supported by creativecommons.orgcall_made


Cite this manuscript as
Vikramsingh Parihar, Roshani Nage, Atul Dahane, "A Novel Graph-based Image Mining Technique Using Weighted Substructure," in Image Segmentation: A Guide to Image Mining, 1st ed., ITIPPR: ICSES, 2018, pp. 16-25.


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Manuscript ID: 218
Pages: 16-25
Submitted: 2018-08-28
Revised: 2018-12-30
Accepted: 2018-12-31
Published: 2018-12-30

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Journal's Title
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ICSES Transactions on Image Processing and Pattern Recognition
ISSN: 2645-8071

ISSN: 2645-8071
Frequency: Quarterly
Accessability: Online - Open Access
Founded in: Mar. 2015
Publisher: ICSES
DOI Suffix: 10.31424/icses.itippr