Image indexing using composite regional color channel features

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Abstract
Color indexing is a technique by which images in the database could be retrieved on the basis of their color content. In this paper, we propose a new set of color features for representing color images, and show how they can be computed and used efficiently to retrieve images that possess certain similarity. These features are based on the first three moments of each color channel. Two differences distinguish this work from previous work reported in the literature. First, we compute the third moment of the color channel distribution around the second moment, not around the first moment. The second moment is less sensitive to small luminance changes, than the first moment. Secondly, we combine all three moment values in a single descriptor. This reduces the number of floating point values needed to index the image and, hence, speeds up the search. To give the user flexibility in terns of defining his center of attention during query time, the proposed approach divides the image into five geometrical regions and allows the user of give different weights for each region to designate its importance. The approach has been tested on databases of 205 images of airplanes and natural scenes. It proved to be insensitive to small rotations and small translations in the image and yielded a better hit rate than similar algorithms previously reported in the literature.

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