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Image Classification using Frequent Itemset Mining

by Vyoma Patel, G. J. Sahani
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 121 - Number 15
Year of Publication: 2015
Authors: Vyoma Patel, G. J. Sahani
10.5120/21614-4880

Vyoma Patel, G. J. Sahani . Image Classification using Frequent Itemset Mining. International Journal of Computer Applications. 121, 15 ( July 2015), 7-11. DOI=10.5120/21614-4880

@article{ 10.5120/21614-4880,
author = { Vyoma Patel, G. J. Sahani },
title = { Image Classification using Frequent Itemset Mining },
journal = { International Journal of Computer Applications },
issue_date = { July 2015 },
volume = { 121 },
number = { 15 },
month = { July },
year = { 2015 },
issn = { 0975-8887 },
pages = { 7-11 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume121/number15/21614-4880/ },
doi = { 10.5120/21614-4880 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:08:29.655072+05:30
%A Vyoma Patel
%A G. J. Sahani
%T Image Classification using Frequent Itemset Mining
%J International Journal of Computer Applications
%@ 0975-8887
%V 121
%N 15
%P 7-11
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Image classification is one of the most useful and essential research field in computer vision domain and challenging task in the image management and retrieval system. The growing demands for image classification in computer vision having application such as video surveillance, image and video retrieval, web content analysis, biometrics etc. have pushed application developers to search and classify images more efficiently. The main goal of image classification is to classifying image into different classes according to their visual characteristics. In this paper, we propose an approach for image classification by applying frequent itemset mining. Frequent Itemset Mining is used to finding the frequent patterns which is referring as Frequent Local Histograms or FLHs. All Local Histogram information must be maintained for obtaining these FLHs patterns and demonstrate the Bag-of-FLHs based image representation. The main aim of this research is to use PCA-SIFT (Principal Component Analysis- Scale Invariant Feature Transform) local descriptor for feature extraction. It is more distinctive, robust to image deformations and more compact than the standard SIFT descriptor. The proposed work reduces the overall time of image classification task and maintaining its overall accuracy.

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Index Terms

Computer Science
Information Sciences

Keywords

SIFT PCA-SIFT BOW

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