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NTF vs. PCA Features for Searching in a Spectral Image Database

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A technique for searching in a spectral image database is proposed in this study. It is based on a similarity measure between spectral image features. New and convenient spectral image features are introduced and compared here. Nonnegative tensor factorization (NTF) and principal component analysis (PCA) are applied in a spectral image domain to characterize colors of a spectral image. A new way of NTF with a multiresolution approach is used to accelerate the time complexity in the extraction of the features.

The proposed method is implemented and tested with a spectral image database. The images from the database are ordered according to the similarity between them and the tested image. Three similarity measures were applied in the two spectral image feature spaces. The results of the experiments are visually represented. The best combination of the spectral image feature and similarity measure in our opinion is proposed during a discussion part. Also further work will be proposed.
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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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