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Multi-Query Content Based Image Retrieval System using Local Binary Patterns

by Simily Joseph, Kannan Balakrishnan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 17 - Number 7
Year of Publication: 2011
Authors: Simily Joseph, Kannan Balakrishnan
10.5120/2235-2857

Simily Joseph, Kannan Balakrishnan . Multi-Query Content Based Image Retrieval System using Local Binary Patterns. International Journal of Computer Applications. 17, 7 ( March 2011), 1-5. DOI=10.5120/2235-2857

@article{ 10.5120/2235-2857,
author = { Simily Joseph, Kannan Balakrishnan },
title = { Multi-Query Content Based Image Retrieval System using Local Binary Patterns },
journal = { International Journal of Computer Applications },
issue_date = { March 2011 },
volume = { 17 },
number = { 7 },
month = { March },
year = { 2011 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume17/number7/2235-2857/ },
doi = { 10.5120/2235-2857 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:04:56.462357+05:30
%A Simily Joseph
%A Kannan Balakrishnan
%T Multi-Query Content Based Image Retrieval System using Local Binary Patterns
%J International Journal of Computer Applications
%@ 0975-8887
%V 17
%N 7
%P 1-5
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Content Based Image Retrieval systems open new research areas in Computer Vision due to the high demand of image searching methods. CBIR is the process of finding relevant image from large collection of images using visual queries. The proposed system uses multiple image queries for finding desired images from database. The different queries are connected using logical AND operation. Local Binary Pattern (LBP) texture descriptors of the query images are extracted and those features are compared with the features of the images in the database for finding the desired images. The proposed system is used for retrieving similar human face expressions. The use of multiple queries reduces the semantic gap between low level visual features and high level user expectation. The experimental result shows that, the use of multiple queries has better retrieval performance over single image queries.

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Index Terms

Computer Science
Information Sciences

Keywords

Feature Extraction Query By Example Texture Analysis

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