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Retrieval of Image Databases Using Supervised Learning Approach

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Retrieval of images from large collections of image databases based on the image content has become an important issue for database and image processing communities. It is generally accepted that texture and color are the key features for the Image Retrieval Systems. Normally texture is extracted from gray scale images and the contribution of color to the perception of texture has been ignored mostly. The paper proposes a method which combines the human perception of color and texture employing supervised learning to obtain an overall impression of image. The initial results of the proposed technique found to be promising. Some of the results have been reported in the paper.
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Document Type: Research Article

Publication date: January 1, 2002

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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