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Image Retrieval using Entropy

by NST Sai, R. C. Patil
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 24 - Number 8
Year of Publication: 2011
Authors: NST Sai, R. C. Patil
10.5120/2992-3901

NST Sai, R. C. Patil . Image Retrieval using Entropy. International Journal of Computer Applications. 24, 8 ( June 2011), 42-49. DOI=10.5120/2992-3901

@article{ 10.5120/2992-3901,
author = { NST Sai, R. C. Patil },
title = { Image Retrieval using Entropy },
journal = { International Journal of Computer Applications },
issue_date = { June 2011 },
volume = { 24 },
number = { 8 },
month = { June },
year = { 2011 },
issn = { 0975-8887 },
pages = { 42-49 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume24/number8/2992-3901/ },
doi = { 10.5120/2992-3901 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:10:29.544851+05:30
%A NST Sai
%A R. C. Patil
%T Image Retrieval using Entropy
%J International Journal of Computer Applications
%@ 0975-8887
%V 24
%N 8
%P 42-49
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Content based image retrieval address the problem of retrieving images relevant to the user needs from image databases on the basis of low-level visual features that can be derived from the images. Grouping images into meaningful categories to reveal useful information is a challenging and important problem. This paper represent effective approaches for Content Based Image Retrieval (CBIR) that represent each image in the database by feature vector computed by using entropy of sub block of bit plane image. This paper present four method for calculating the feature vector of image .All the proposed methods tested on image database which include 800 images with 8 classes. Euclidean distance is used for similarity measure. Performance of each method calculated by using overall average precision and overall average recall for comparison of proposed method. Overall precision and overall recall computed using 40 queries on the image database. It is found that as the image divide into more sub block the performance goes on increasing.

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Index Terms

Computer Science
Information Sciences

Keywords

CBIR Entropy Bit Plane Image Sub block Precision Recall Euclidean Distance

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