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Framework for forgery detection in content based image retrieval system


Article Information

Title: Framework for forgery detection in content based image retrieval system

Authors: M. Sivakumar, P. Renuga, D. Kumaran, N. Venkatesh

Journal: ARPN Journal of Engineering and Applied Sciences

HEC Recognition History
Category From To
Y 2023-07-01 2024-09-30
Y 2022-07-01 2023-06-30
Y 2021-07-01 2022-06-30
X 2020-07-01 2021-06-30

Publisher: Khyber Medical College, Peshawar

Country: Pakistan

Year: 2015

Volume: 10

Issue: 9

Language: English

Keywords: K-MeansB+ treeContent-based image retrieval systems (CBIR)Daubechies’ waveletsZernike moments

Categories

Abstract

Nowadays Image retrieval is becomes more challenged thing in many of the fields like medical diagnosis, crime prevention, military services, architectural and engineering design, geographical information, trade- mark matching, etc. So there is the necessitates of effectively retrieving relevant images when needed. Thus, content-based image retrieval systems (CBIR) have become very popular for browsing, searching and retrieving images from a large database of digital images. So in order to improve the retrieval accuracy, this paper will focus on to use content-based image retrieval systems with k-means as the clustering algorithm and B+ tree for speeding up the retrieval process. For representing the images, we extract their feature vectors of images using Daubechies’ wavelets. Then we introduced the Zernike moments in the retrieved images with the reference image by that we find out the variations of the images. After that by comparing the images using Zernike moments and find out the forgery between the images.


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