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Abstract
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Article Information:
Features Analysis for Content-Based Image Retrieval Based on Color Moments
Fazal Malik and Baharum Baharudin
Corresponding Author: Fazal Malik
Submitted:
Accepted: December 20, 2011
Published: May 01, 2012 |
Abstract:
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In this study an efficient and accurate algorithm is proposed for Content-Based Image Retrieval
(CBIR). The CBIR is performed in two steps: features extraction in images and similarity measurement for
searching of similar images in image database. For efficient and effective CBIR system the features extraction
must be fast and the searching must be accurate. In the proposed algorithm, the effective retrieval of the similar
images from the database is based on the efficient extraction of the local statistical color moment features
without using the spatial features of images. The basic idea in this algorithm is to convert the color RGB (Red
Green and Blue) image into grayscale image to reduce the computations in feature extraction and to increase
the efficiency. The grayscale image is divided into non-overlapping blocks of different sizes. The local
statistical color moment features are extracted in all blocks. The features are combined into a feature vector.
The similarity is measured by using Sum-of-Absolute Difference (SAD) to measure the similarity between
query image and database images. In the experiment, the efficiency of feature extraction and accuracy of the
image retrieval are measured for different block size methods using the proposed algorithm. The Corel database
is used for testing. The results show that the proposed CBIR algorithm provides higher performance in terms
of efficiency and accuracy.
Key words: Color moments, Content-Based Image Retrieval (CBIR), feature vector, Sum-of-Absolute Difference (SAD), , ,
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Abstract
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Cite this Reference:
Fazal Malik and Baharum Baharudin, . Features Analysis for Content-Based Image Retrieval Based on Color Moments. Research Journal of Applied Sciences, Engineering and Technology , (09): 1215-1224.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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