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Compact color descriptor for object recognition across illumination changes

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In this paper, we propose a compact color invariant image descriptor which characterizes both the color distribution and the spatial interactions between the pixels in the image. Our approach is based on the analysis of the pixel rank measures which are their ranks when they are sorted according to their color component levels within a color image. Indeed, we show in this paper that the correlation between the rank measures of neighbor pixels in a color image is an efficient feature to describe the content of this image. This descriptor, extracted from the chromatic co-occurrences matrices, has the advantages to be invariant to illumination changes, low-time consuming, highly discriminating and compact. The proposed rank correlation coefficient is used by our object recognition scheme whose effectiveness is assessed with a public database that contains images of objects acquired under different illuminations.
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Document Type: Research Article

Publication date: January 1, 2008

More about this publication?
  • 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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