An Efficient Similarity Measure for Color-Based Image Retrieval
Abstract<br /> Similarity measures are an important factor in the Content-Based Image Retrieval (CBIR). This paper finds the most efficient similarity measure from four image similarity measures. Related work on (CBIR) indicated that these measures have significantly improved the retrieval per...
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| Main Authors: | , |
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| Format: | Book |
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College of Education for Pure Sciences,
2008-06-01T00:00:00Z.
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| Summary: | Abstract<br /> Similarity measures are an important factor in the Content-Based Image Retrieval (CBIR). This paper finds the most efficient similarity measure from four image similarity measures. Related work on (CBIR) indicated that these measures have significantly improved the retrieval performance. These measures are the Chi-Squared, The Weighted Mean Variance distance (WMV), The Euclidean distance, and Cosine distance. A sample of 50 colored images is selected from CALTECH visual database. These images were transformed to (HSV) color space. Color features were extracted; these features are the color moments. Experimental results of the proposed work show that the Euclidean distance measure is the most efficient measure for color based image retrieval. |
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| Item Description: | 1812-125X 2664-2530 10.33899/edusj.2008.51283 |