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http://hdl.handle.net/20.500.12358/24456
TitleContent-based image retrieval using invariant color and texture features
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Abstract

Since the last decade, Content-Based Image Retrieval was a hot topic research. The computational complexity and the retrieval accuracy are the main problems that CBIR systems have to avoid. To avoid these problems, this paper proposes a new content-based image retrieval method that uses both color and texture feature. To extract the color feature from the image, the color moment will be calculated where the image will be in the HSV color space. To extract the texture feature, the image will be in gray-scale and Ranklet Transform is performed on it. From the ranklet images generated from the original image, the texture feature is extracted by calculating the texture moments. Experiments results show that using both color and texture feature to describe the image and use them for image retrieval is more accurate than using one of them only.

Authors
Al-Afifi, Ahmed A.
Ashour, Wesam M.
TypeJournal Article
Date2012
Published inDigital Image Computing Techniques and Applications (DICTA), 2012 International Conference on
PublisherIEEE
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  • Staff Publications- Faculty of Engineering [865]
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The institutional repository of the Islamic University of Gaza was established as part of the ROMOR project that has been co-funded with support from the European Commission under the ERASMUS + European programme. This publication reflects the views only of the author, and the Commission cannot be held responsible for any use which may be made of the information contained therein.

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The institutional repository of the Islamic University of Gaza was established as part of the ROMOR project that has been co-funded with support from the European Commission under the ERASMUS + European programme. This publication reflects the views only of the author, and the Commission cannot be held responsible for any use which may be made of the information contained therein.

Contact Us | Send Feedback