Technique for Searching Images in a Spectral Image Database

Markku Hauta-Kasari, Kanae Miyazawa *, Jussi Parkkinen, and Timo Jaaskelainen

University of Joensuu Color GroupDepartment 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 JoensuuMarkku Hauta-Kasari: Visiting researcher 1996-1998 at Prof. Toyooka lab.

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

Contents

1. Introduction2. Searching Technique3. Experimental Results4. Conclusions

IntroductionAccurate 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 StudyTo propose a technique for searching images in a spectral image database

Experimental data76 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

IntroductionSpectral image databases expanding: fast methods for searching images will be needed in

the future

The Aim of this StudyTo propose a technique for searching images in a spectral image database

Experimental data76 real-world spectral images

Searching Technique

Histogram database creation

1. Select spectra randomly, train a SOM2. Calculate BMU-images and BMU-histograms

Search

3. Select a wanted image, calculate BMU-image and BMU-histogram4. Calculate histogram similarities5. Order the search results based on the similarities

Filter = Light Source

Sample

Self-Organized Map

Filter = Light Source

Sample

SOM-units in CIELAB

Filter = Light Source

Sample

Example of BMU-image

Filter = Light Source

Sample

Example of BMU-histogram

Filter = Light Source

Sample

Example of Search

Filter = Light Source

Sample

Histogram Differences

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

More information

http://cs.joensuu.fi/spectral

Email: mhk@cs.joensuu.fi