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Intelligent Bilddatabassökning

Intelligent Bilddatabassökning. Reiner Lenz, Thanh H. Bui, (Linh V. Tran) ITN, Linköpings Universitet David Rydén, Göran Lundberg Matton AB, Stockholm. Image database. Query image. isual Information Retrieval. The growth of the Internet and digital image collections.

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Intelligent Bilddatabassökning

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

  2. Image database Query image isual Information Retrieval • The growth of the Internet and digital image collections Requires efficient image data management Search Similar Images Reiner Lenz, Intelligent Bilddatabassökning, Vinnova Programkonferens 2004

  3. eed an image of a tiger Reiner Lenz, Intelligent Bilddatabassökning, Vinnova Programkonferens 2004

  4. Reiner Lenz, Intelligent Bilddatabassökning, Vinnova Programkonferens 2004

  5. atton http://www.matton.se/ Reiner Lenz, Intelligent Bilddatabassökning, Vinnova Programkonferens 2004

  6. eyword-based approach • Advantages • Use existing text-based techniques • 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 Reiner Lenz, Intelligent Bilddatabassökning, Vinnova Programkonferens 2004

  7. 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. 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. Reiner Lenz, Intelligent Bilddatabassökning, Vinnova Programkonferens 2004

  8. Compute colordescriptors Compute colordescriptors Match Engine ImageDatabase Retrieved result Query image olor-based Image Retrieval • Describe color information of images • Measure the similarity between images Reiner Lenz, Intelligent Bilddatabassökning, Vinnova Programkonferens 2004

  9. More parameters Slower search Requires more memory Better retrieval  performance Less parameters • Fastersearch  Requires less memory Reduced retrievalperformance Trade-off Our aim roblems Developed algorithms to Describe images Measure similarities Combine both Faster search Better retrieval performance Reiner Lenz, Intelligent Bilddatabassökning, Vinnova Programkonferens 2004

  10. 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 Reiner Lenz, Intelligent Bilddatabassökning, Vinnova Programkonferens 2004

  11. ext • Overview • Describe color information = estimating color distributions • Measuring the distances between color distributions • Compressing the color feature space • Current indexing techniques O(log2n) • 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 Reiner Lenz, Intelligent Bilddatabassökning, Vinnova Programkonferens 2004

  12. Query image Ground truth images xperiments: Image database • MPEG-7 database of 5466 images • 50 standard queries • Quality measure Reiner Lenz, Intelligent Bilddatabassökning, Vinnova Programkonferens 2004

  13. xperiments Reiner Lenz, Intelligent Bilddatabassökning, Vinnova Programkonferens 2004

  14. ngines • Currently we have 3 big search engines • 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-imgdb Thesis: http://www.ep.liu.se/diss/science_technology/08/10 Text-based browser: Matton http://www.matton.se • Compression using local differences • Compression using normal PCA and normalization 405933 images from Matton AB, Stockholm Reiner Lenz, Intelligent Bilddatabassökning, Vinnova Programkonferens 2004

  15. 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 objectsillumination, camera, etc. Reiner Lenz, Intelligent Bilddatabassökning, Vinnova Programkonferens 2004

  16. ight material interaction • Involves many complicated processes • Reflection • Refraction • Absorption • Scattering • Emission • etc. • Models • Dichromatic reflection model • Kubelka-Munk model Reiner Lenz, Intelligent Bilddatabassökning, Vinnova Programkonferens 2004

  17. obust region merging Reiner Lenz, Intelligent Bilddatabassökning, Vinnova Programkonferens 2004

  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

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