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Reseach Article

Density Distribution in Walsh Transform Sectors as Feature Vectors for Image Retrieval

by Dhirendra Mishra, H.B.Kekre
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 4 - Number 6
Year of Publication: 2010
Authors: Dhirendra Mishra, H.B.Kekre
10.5120/829-1072

Dhirendra Mishra, H.B.Kekre . Density Distribution in Walsh Transform Sectors as Feature Vectors for Image Retrieval. International Journal of Computer Applications. 4, 6 ( July 2010), 30-36. DOI=10.5120/829-1072

@article{ 10.5120/829-1072,
author = { Dhirendra Mishra, H.B.Kekre },
title = { Density Distribution in Walsh Transform Sectors as Feature Vectors for Image Retrieval },
journal = { International Journal of Computer Applications },
issue_date = { July 2010 },
volume = { 4 },
number = { 6 },
month = { July },
year = { 2010 },
issn = { 0975-8887 },
pages = { 30-36 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume4/number6/829-1072/ },
doi = { 10.5120/829-1072 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:52:24.451586+05:30
%A Dhirendra Mishra
%A H.B.Kekre
%T Density Distribution in Walsh Transform Sectors as Feature Vectors for Image Retrieval
%J International Journal of Computer Applications
%@ 0975-8887
%V 4
%N 6
%P 30-36
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents the idea of using sal cal density distribution in complex Walsh transform sectors to generate the feature vector for content based image retrieval This paper compares the performance of 8 , 12 and 16 sectors of Walsh Transform. The density distribution of real (sal) and imaginary (cal) values of Walsh sectors in all three color planes are considered to design the feature vector. The algorithm proposed here is worked over database of 270 images spread over 11 different classes. The Euclidean distance is used as similarity measure. Overall Average precision and recall is calculated for the performance evaluation and comparison of 8, 12 & 16 Walsh sectors. The overall average of cross over points of precision and recall is of all methods are compared.

References
Index Terms

Computer Science
Information Sciences

Keywords

CBIR Walsh Transform Euclidian Distance Precision Recall