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EXTRACTION OF MAIN COLORS FROM A COLOR DIGITAL IMAGE

AUTHOR/S: A. CIOBANU, M. COSTIN, T. BARBU
Sunday 1 August 2010 by Libadmin2010

10th International Multidisciplinary Scientific GeoConference - SGEM2010, www.sgem.org, SGEM2010 Conference Proceedings/ ISBN 10: 954-91818-1-2, June 20-26, 2010, Vol. 1, 1069-1076 pp

ABSTRACT

In order to prepare minimal feature vectors to be used in a Content Based Image
Recognition (CBIR) system, we are proposing methods that can accurately identify the
main colors contained in a digital image. The methods use the “LAB” color space
properties that allow for all the colors of the same intensity or luminance “L” to be
placed uniformly in the same “ab” plane. Basically, RGB images are first preprocessed:
trimmed, scaled and transformed in the LAB color space. Then (a,b) pairs,
corresponding to large pixel counts in the image, are detected using two or more planar
histograms. One of our approaches determines individual (L,a,b) triplets (each defining
precisely a color) belonging to dark and light colors, and treats separately black, white
and gray tones. It makes three lists of colors and then reduces the number of colors in
each list by merging close shades of colors until only several main colors are listed
globally for the processed image. Another advanced approach finds (a,b) pairs in five
preset bands of luminance “L”, resulting in five lists of colors that are also reduced in
number by merging shades of colors in each band. The top colors in each band (or, if
necessary, top 3 colors) are then extracted as color features for an image. In both
approaches, the initial image is reconstructed with the identified main colors for visual
inspection and, as a byproduct, for extracting edges with a Canny detector. Results show
potential for further use in image classification or retrieval, as well as for segmentation
of images in regions.

Keywords: LAB color space, uniform color regions, edge detection, color features, CBIR

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