1. 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. Optics in Engineering, OIE’03, Saariselkä, FINLAND, August 7, 2003

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 the future 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 the future 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. Filter = Light Source Sample Self-Organized Map

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

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

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

10. Filter = Light Source Sample Example of Search

11. Filter = Light Source Sample Histogram Differences

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