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Reseach Article

An Improved CBIR based on Color and Spatial Feature with Relevance Feedback

Published on August 2011 by Asmita Deshmukh
journal_cover_thumbnail
National Technical Symposium on Advancements in Computing Technologies
Foundation of Computer Science USA
NTSACT - Number 4
August 2011
Authors: Asmita Deshmukh
daa3c376-e4ef-457b-b326-70918af2349d

Asmita Deshmukh . An Improved CBIR based on Color and Spatial Feature with Relevance Feedback. National Technical Symposium on Advancements in Computing Technologies. NTSACT, 4 (August 2011), 1-5.

@article{
author = { Asmita Deshmukh },
title = { An Improved CBIR based on Color and Spatial Feature with Relevance Feedback },
journal = { National Technical Symposium on Advancements in Computing Technologies },
issue_date = { August 2011 },
volume = { NTSACT },
number = { 4 },
month = { August },
year = { 2011 },
issn = 0975-8887,
pages = { 1-5 },
numpages = 5,
url = { /proceedings/ntsact/number4/3211-ntst032/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Technical Symposium on Advancements in Computing Technologies
%A Asmita Deshmukh
%T An Improved CBIR based on Color and Spatial Feature with Relevance Feedback
%J National Technical Symposium on Advancements in Computing Technologies
%@ 0975-8887
%V NTSACT
%N 4
%P 1-5
%D 2011
%I International Journal of Computer Applications
Abstract

The CBIR problem is motivated by the need to search the exponentially increasing space of image and image databases efficiently and effectively. The survey feature extraction and selection techniques adopted in content based image retrieval (CBIR), is a technique that uses the visual content of a still image to search for similar images in large scale image databases, according to a user’s interest. The visual content of an image is analyzed primarily in terms of low level features extracted from the image which constitute color, texture and shape features. A novel color image retrieval method using both color and local spatial feature histograms (CLSFH) is proposed in this paper. In CLSFH, the non uniform quantized HSV color model is used, the mean, the standard deviation of 5x5 neighbor of every pixel are calculated, and are used to generate the Local Mean Histogram, the Local Standard Deviation Histogram; is defined and computed using the proposed algorithm. A relevance feedback approach that attempts to bridge the gap between low level features extracted from an image and high level semantic features. By integrating the user feedback information, the feature selection is able to bridge the gap between low-level visual features and high-level semantic information, leading to the improved image retrieval accuracy.

References
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Index Terms

Computer Science
Information Sciences

Keywords

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