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Comparative Study and Optimization of Feature-Extraction Techniques for Content based Image Retrieval

by Aman Chadha, Sushmit Mallik, Ravdeep Johar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 52 - Number 20
Year of Publication: 2012
Authors: Aman Chadha, Sushmit Mallik, Ravdeep Johar
10.5120/8320-1959

Aman Chadha, Sushmit Mallik, Ravdeep Johar . Comparative Study and Optimization of Feature-Extraction Techniques for Content based Image Retrieval. International Journal of Computer Applications. 52, 20 ( August 2012), 35-42. DOI=10.5120/8320-1959

@article{ 10.5120/8320-1959,
author = { Aman Chadha, Sushmit Mallik, Ravdeep Johar },
title = { Comparative Study and Optimization of Feature-Extraction Techniques for Content based Image Retrieval },
journal = { International Journal of Computer Applications },
issue_date = { August 2012 },
volume = { 52 },
number = { 20 },
month = { August },
year = { 2012 },
issn = { 0975-8887 },
pages = { 35-42 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume52/number20/8320-1959/ },
doi = { 10.5120/8320-1959 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:52:47.555615+05:30
%A Aman Chadha
%A Sushmit Mallik
%A Ravdeep Johar
%T Comparative Study and Optimization of Feature-Extraction Techniques for Content based Image Retrieval
%J International Journal of Computer Applications
%@ 0975-8887
%V 52
%N 20
%P 35-42
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The aim of a Content-Based Image Retrieval (CBIR) system, also known as Query by Image Content (QBIC), is to help users to retrieve relevant images based on their contents. CBIR technologies provide a method to find images in large databases by using unique descriptors from a trained image. The image descriptors include texture, color, intensity and shape of the object inside an image. Several feature-extraction techniques viz. , Average RGB, Color Moments, Co-occurrence, Local Color Histogram, Global Color Histogram and Geometric Moment have been critically compared in this paper. However, individually these techniques result in poor performance. So, combinations of these techniques have also been evaluated and results for the most efficient combination of techniques have been presented and optimized for each class of image query. We also propose an improvement in image retrieval performance by introducing the idea of Query modification through image cropping. It enables the user to identify a region of interest and modify the initial query to refine and personalize the image retrieval results.

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Index Terms

Computer Science
Information Sciences

Keywords

Feature Extraction Image Similarities Feature Matching Image Retrieval

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