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Technique for Searching Images in a Spectral Image Database

Optics in Engineering, OIE’03, Saariselkä, FINLAND, August 7, 2003. Technique for Searching Images in a Spectral Image Database. Markku Hauta-Kasari, Kanae Miyazawa *, Jussi Parkkinen, and Timo Jaaskelainen University of Joensuu Color Group

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Technique for Searching Images in a Spectral Image Database

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  1. Optics in Engineering, OIE’03, Saariselkä, FINLAND, August 7, 2003 Technique for Searching Images in a Spectral Image Database Markku Hauta-Kasari, Kanae Miyazawa *, Jussi Parkkinen, and Timo Jaaskelainen University of Joensuu Color Group Department of Computer Science, Department of Physics University of Joensuu, FINLAND *Department of Information and Computer Sciences, Toyohashi University of Technology, JAPAN Kanae Miyazawa: PhD from Prof. Toyooka lab., 2001-2003 in Joensuu Markku Hauta-Kasari: Visiting researcher 1996-1998 at Prof. Toyooka lab.

  2. Contents 1. Introduction 2. Searching Technique 3. Experimental Results 4. Conclusions

  3. Introduction Accurate color representation: spectral color representation Spectral image: high-accurate color image, large amount of data Application fields, for example: quality control, telemedicine, e-commerce, electronic museums Spectral image databases expanding: fast methods for searching images will be needed in thefuture The Aim of this Study To propose a technique for searching images in a spectral image database Experimental data 76 real-world spectral images • usually, color filters with fixed transmittances are used: • filters must be physically changed to the system • the transmittance of the filter cannot be changed • computational color filter design: • the transmittance of the filter can be adaptive to an application spectral imaging systems that can use arbitrary rewritable color filters are needed

  4. Introduction Spectral image databases expanding:  fast methods for searching images will be needed in thefuture The Aim of this Study To propose a technique for searching images in a spectral image database Experimental data 76 real-world spectral images

  5. Searching Technique Histogram database creation 1. Select spectra randomly, train a SOM 2. Calculate BMU-images and BMU-histograms Search 3. Select a wanted image, calculate BMU-image and BMU-histogram 4. Calculate histogram similarities 5. Order the search results based on the similarities

  6. Self-Organized Map Filter = Light Source Sample

  7. SOM-units in CIELAB Filter = Light Source Sample

  8. Example of BMU-image Filter = Light Source Sample

  9. Example of BMU-histogram Filter = Light Source Sample

  10. Example of Search Filter = Light Source Sample

  11. Histogram Differences Filter = Light Source Sample

  12. Conclusions • The histogram database is generated once for a certain spectral image database. This is a time consuming phase. • The searching technique is fast and it preserves the spectral color information. The searching time for synthetically generated 1000 spectral image test set was 1 second (Matlab, Linux PC) Future: • to test more features for BMU-histogram similarity calculation • to add textural features to the technique

  13. More information http://cs.joensuu.fi/spectral Email: mhk@cs.joensuu.fi

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