Analysis of Distance Measures in Content based Image Retrieval

Anjali Batra; Dr. Meenakshi Sharma

Analysis of Distance Measures in Content based Image Retrieval

Anjali Batra * §

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Dr. Meenakshi Sharma

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§ HCTM TECHNICAL CAMPUS, KAITHAL, HARYANA, INDIA (136027)

Analysis of Distance Measures in Content based Image Retrieval

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CST0358W

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Abstract

Content predicated image retrieval (CBIR) provides an efficacious way to probe the images from the databases. The feature extraction and homogeneous attribute measures are the two key parameters for retrieval performance. A homogeneous attribute measure plays a paramount role in image retrieval. This paper compares six different distance metrics such as Euclidean, Manhattan, Canberra, Bray-Curtis, Square chord, Square chi-squared distances to find the best kindred attribute measure for image retrieval. Utilizing pyramid structured wavelet decomposition, energy levels are calculated. These energy levels are compared by calculating distance between query image and database images utilizing above mentioned seven different kindred attribute metrics. A sizably voluminous image database from Brodatz album is utilized for retrieval purport. Experimental results shows the preponderating of Canberra, Bray-Curtis, Square chord, and Square Chi-squared distances over the conventional Euclidean and Manhattan distances.

References

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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

Anjali Batra, Dr. Meenakshi Sharma. 2014. "Analysis of Distance Measures in Content based Image Retrieval". Global Journal of Computer Science and Technology - G: Interdisciplinary GJCST-G Volume 14 (GJCST Volume 14 Issue G2).

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GJCST Volume 14 Issue G2

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

CBIR chebychev distance cityblock distance Confusion matrix distance metrics euclidean distance mahalanobis distance manhattan distance

Classification

I.4.10

Submission ReceivedDecember 7, 2013
Peer Review Double Blind
Handling Editor
Accepted January 3, 2014
Published January 15, 2014

Version of record

v1.2

Issue date

September 18, 2014

Language

English

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Article Matrices

Total Score: 147

Country: India

Subject: Global Journal of Computer Science and Technology

Authors: Dr. Meenakshi Sharma, Anjali Batra (PhD/Dr. count: 1)

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