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Utilization of Spectral Information in Clustering based Color Image Segmentation

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We studied the utilization of illumination and observer spectral characteristics in clustering based color image segmentation. Segmentation was based on k-means algorithm, spectral clustering algorithm and non-local spatial constraint spectral clustering algorithm both with the Nyström method. The image segmentations were performed for four different representations derived from a set of spectral images: 1) simulated sRGB images using D65 illumination, 2) six band images based on sRGB images under the D65 and A illuminations, 3) estimated spectral reflectance images, and 4) true spectral reflectance images. The spectral reflectance estimation was based on Wiener estimation model, reflectance of Macbeth Color Checker (24 samples) and CIE XYZ tristimulus values under the D65 and A illuminations. The experimental results showed that the segmentation results via reflectance estimation model were improved when compared to the segmentation with sRGB images. These results suggest that it is useful to include the knowledge from illumination and observer spectral characteristics in order to increase the clustering based color image segmentation accuracy.
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Document Type: Research Article

Publication date: January 1, 2012

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  • Started in 2002 and merged with the Color and Imaging Conference (CIC) in 2014, CGIV covered a wide range of topics related to colour and visual information, including color science, computational color, color in computer graphics, color reproduction, volor vision/psychophysics, color image quality, color image processing, and multispectral color science. Drawing papers from researchers, scientists, and engineers worldwide, DGIV offered attendees a unique experience to share with colleagues in industry and academic, and on national and international standards committees. Held every year in Europe, DGIV papers were more academic in their focus and had high student participation rates.

    Please note: For purposes of its Digital Library content, IS&T defines Open Access as papers that will be downloadable in their entirety for free in perpetuity. Copyright restrictions on papers vary; see individual papers for details.

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