1. Intelligent Bilddatabassökning Reiner Lenz, Thanh H. Bui, (Linh V. Tran) ITN, Linköpings Universitet David Rydén, Göran Lundberg Matton AB, Stockholm

2. Reiner Lenz, Intelligent Bilddatabassökning, Vinnova Programkonferens 20042 Requires efficient image data management Search Similar Images isual Information Retrieval The growth of the Internet and digital image collections Query image Image database

3. Reiner Lenz, Intelligent Bilddatabassökning, Vinnova Programkonferens 20043 eed an image of a tiger

4. Reiner Lenz, Intelligent Bilddatabassökning, Vinnova Programkonferens 20044

5. 5 atton http://www.matton.se/

6. Reiner Lenz, Intelligent Bilddatabassökning, Vinnova Programkonferens 20046 eyword-based approach Disadvantages  Very large and sophisticated keyword systems  Require well-trained personnel to Annotate keywords to each image in the database Select good keywords in retrieval phase  Manual annotation Time consuming Costly Dependent on the subjectivity of human perception  Very hard to change once annotations are done Advantages  Use existing text-based techniques

7. Reiner Lenz, Intelligent Bilddatabassökning, Vinnova Programkonferens 20047 ontent-based Approach Content-Based Image Retrieval: CBIR Fundamental idea: generate automatically image descriptions by analyzing the visual content of the images CBIR: very active research field  Describing images  Similarity measure  Query analysis  Indexing techniques  System design  etc. Visual features  Low-level features Color Texture Shape, etc.  High-level features  Application-oriented features Face, hand-geometry, trademark recognition, etc. CBIR: very active research field  Describing images  Similarity measure  Query analysis  Indexing techniques  System design  etc. Visual features  Low-level features Color Texture Shape, etc.  High-level features  Application-oriented features Face, hand-geometry, trademark recognition, etc.

8. Reiner Lenz, Intelligent Bilddatabassökning, Vinnova Programkonferens 20048 olor-based Image Retrieval Describe color information of images Measure the similarity between images Query image Image Database Match Engine Compute color descriptors Retrieved result

9. Reiner Lenz, Intelligent Bilddatabassökning, Vinnova Programkonferens 20049 Less parameters  Faster search  Requires less memory  Reduced retrieval performance More parameters Slower search  Requires more memory  Better retrieval  performance  Trade-off Developed algorithms to  Describe images  Measure similarities  Combine both Better retrieval performance Faster search Our aim roblems

10. Reiner Lenz, Intelligent Bilddatabassökning, Vinnova Programkonferens 200410 ext Overview  Describe color information = estimating color distributions  Measuring the distances between color distributions Take into account: A) Distance measures between statistical distributions B) Distance measures that take into account color

11. Reiner Lenz, Intelligent Bilddatabassökning, Vinnova Programkonferens 200411 ext Overview  Describe color information = estimating color distributions  Measuring the distances between color distributions  Compressing the color feature space Current indexing techniques O(log 2 n) -More than 20 dimensions: Slow sequential search O(n) Given -a method to describe color images and -a way to measure the similarity between images Find a compression method with small loss in retrieval performance

12. Reiner Lenz, Intelligent Bilddatabassökning, Vinnova Programkonferens 200412 MPEG-7 database of 5466 images 50 standard queries Quality measure xperiments: Image database Query image Ground truth images

13. Reiner Lenz, Intelligent Bilddatabassökning, Vinnova Programkonferens 200413 xperiments

14. Reiner Lenz, Intelligent Bilddatabassökning, Vinnova Programkonferens 200414 ngines Currently we have 3 big search engines  Linköping University Electronic Press Linköping University Electronic Press Search engine developed as part of L. V. Tran’s PhD thesis based on 126604 images from Matton AB, Stockholm Old Search Engine: http://www.ep.liu.se/databases/cse-imgdbhttp://www.ep.liu.se/databases/cse-imgdb Thesis: http://www.ep.liu.se/diss/science_technology/08/10http://www.ep.liu.se/diss/science_technology/08/10 Text-based browser: Matton http://www.matton.seMatton  Compression using local differences Compression using local differences  Compression using normal PCA and normalization Compression using normal PCA and normalization 405933 images from Matton AB, Stockholm

15. Reiner Lenz, Intelligent Bilddatabassökning, Vinnova Programkonferens 200415 olor invariant features Color of images depends on many factors  Illumination of the scene  Spectral properties of the objects  Characteristics of the camera sensors  Geometrical properties of the objects illumination, camera, etc.

16. Reiner Lenz, Intelligent Bilddatabassökning, Vinnova Programkonferens 200416 ight material interaction Involves many complicated processes  Reflection  Refraction  Absorption  Scattering  Emission  etc. Models  Dichromatic reflection model  Kubelka-Munk model

17. Reiner Lenz, Intelligent Bilddatabassökning, Vinnova Programkonferens 200417 obust region merging

18. Five original images are in the diagonal Five different illuminations: Mb-5000+3202 Mb-5000 Ph-ulm Syl-cwf Halogen Images in the same column are corrected to the same illumination