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Reseach Article

Content based Image Retrieval System with Watermarks and Relevance Feedback

by Sebin Jose, Philumon Joseph
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 99 - Number 11
Year of Publication: 2014
Authors: Sebin Jose, Philumon Joseph
10.5120/17414-8197

Sebin Jose, Philumon Joseph . Content based Image Retrieval System with Watermarks and Relevance Feedback. International Journal of Computer Applications. 99, 11 ( August 2014), 1-6. DOI=10.5120/17414-8197

@article{ 10.5120/17414-8197,
author = { Sebin Jose, Philumon Joseph },
title = { Content based Image Retrieval System with Watermarks and Relevance Feedback },
journal = { International Journal of Computer Applications },
issue_date = { August 2014 },
volume = { 99 },
number = { 11 },
month = { August },
year = { 2014 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume99/number11/17414-8197/ },
doi = { 10.5120/17414-8197 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:27:54.493381+05:30
%A Sebin Jose
%A Philumon Joseph
%T Content based Image Retrieval System with Watermarks and Relevance Feedback
%J International Journal of Computer Applications
%@ 0975-8887
%V 99
%N 11
%P 1-6
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Image retrieval and related operations are always a 'hotspot' in the information era. Content-based image retrieval (CBIR) is a vastly developing area in the multimedia technology domain. To enhance security, we apply watermarking technique into the retrieval system and propose an approach for JEPG image retrieval. The proposed image retrieval system consists of three main phases, offline process, online retrieval process and the feedback process. The offline process aims at the feature vector extraction from the image. Later these features will be stored in the database. When it comes to the online retrieval process, it actually extracts the image features from the input image and matches these feature vectors with those available in the image database. In order to overcome the possible dissimilarity between bottom features and high-level semantics in the image retrieval; we introduce the feedback network to strengthen the retrieval efficiency. This is a simple categorized screening. Such a feedback scenario makes the system more user-friendly and effective. The proposed feedback screening strategy filters the images from irrelevant categories and enriches the final result with more relevant images

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Index Terms

Computer Science
Information Sciences

Keywords

Content Image