A Comparative Study of Image Retrieval Algorithms for Enhancing a Content-Based Image Retrieval System

A Comparative Study of Image Retrieval Algorithms for Enhancing a Content-Based Image Retrieval System

Elsaeed E. AbdElrazek

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A Comparative Study of Image Retrieval Algorithms for Enhancing a Content-Based Image Retrieval System

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Abstract

Content Based image retrieval (CBIR) is in retrieve digital images by the actual content in the image. The content are the features of the image such as color, shape, texture and other information about the image including some statistic measures of the image. In this paper Content Based Image Retrieval algorithms are discussed. The comparative study of these algorithms is done. This article covers various techniques for implementing Content Based Image Retrieval algorithms and Some Open Source examples of Content-based Image Retrieval Search Engines.

References

17 Cites in Article

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Elsaeed E. AbdElrazek. 2018. \u201cA Comparative Study of Image Retrieval Algorithms for Enhancing a Content-Based Image Retrieval System\u201d. Global Journal of Computer Science and Technology - F: Graphics & Vision GJCST-F Volume 17 (GJCST Volume 17 Issue F3): .

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GJCST Volume 17 Issue F3
Pg. 1- 8

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

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Classification

B.4.2, H.2.8, I.3.3

Submission ReceivedDecember 14, 2016
Peer Review Double Blind
Handling Editor
Accepted January 3, 2017
Published January 15, 2017

Version of record

v1.2

Issue date

January 17, 2018

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Article Matrices

Total Score: 101

Country: Egypt

Subject: Global Journal of Computer Science and Technology - F: Graphics & Vision

Authors: Elsaeed E. AbdElrazek (PhD/Dr. count: 0)

View Count (all-time): 245

Total Views (Real + Logic): 6215

Total Downloads (simulated): 1652

Publish Date: 2018 01, Wed

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Total Views: 6215

Total Downloads: 1652

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