intelligent bilddatabassökning reiner lenz, thanh h. bui, (linh v. tran) itn, linköpings...
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Intelligent Bilddatabassökning
Reiner Lenz, Thanh H. Bui, (Linh V. Tran)ITN, Linköpings Universitet
David Rydén, Göran LundbergMatton AB, Stockholm
Reiner Lenz, Intelligent Bilddatabassökning, Vinnova Programkonferens 2004
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Requires efficient image data managementSearch Similar Images
isual Information Retrieval
The growth of the Internet and digital image collections
Query image
Image database
Reiner Lenz, Intelligent Bilddatabassökning, Vinnova Programkonferens 2004
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eed an image of a tiger
Reiner Lenz, Intelligent Bilddatabassökning, Vinnova Programkonferens 2004
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Reiner Lenz, Intelligent Bilddatabassökning, Vinnova Programkonferens 2004
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atton
http://www.matton.se/
Reiner Lenz, Intelligent Bilddatabassökning, Vinnova Programkonferens 2004
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eyword-based approach
Disadvantages Very large and sophisticated keyword systems Require well-trained personnel to
Annotate keywords to each image in the databaseSelect good keywords in retrieval phase
Manual annotationTime consumingCostlyDependent on the subjectivity of human perception
Very hard to change once annotations are done
Advantages Use existing text-based techniques
Reiner Lenz, Intelligent Bilddatabassökning, Vinnova Programkonferens 2004
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ontent-based Approach
Content-Based Image Retrieval: CBIRFundamental 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.
Reiner Lenz, Intelligent Bilddatabassökning, Vinnova Programkonferens 2004
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olor-based Image Retrieval
Describe color information of imagesMeasure the similarity between images
Query imageImage
Database
Match EngineMatch Engine Compute color
descriptors
Compute color
descriptors
Compute color
descriptors
Compute color
descriptors
Retrieved resultRetrieved result
Reiner Lenz, Intelligent Bilddatabassökning, Vinnova Programkonferens 2004
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Less parameters
Faster search Requires less memory Reduced retrieval performance
More parameters
Slower search Requires more memory
Better retrieval performance
Trade-offTrade-off
Developed algorithms to
Describe images Measure similarities Combine both
Better retrieval performance
Fast
er
searc
h
Our
aim
roblems
Reiner Lenz, Intelligent Bilddatabassökning, Vinnova Programkonferens 2004
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ext
Overview Describe color information = estimating color distributions Measuring the distances between color distributions
Take into account:A) Distance measures between statistical distributionsB) Distance measures that take into account color
Reiner Lenz, Intelligent Bilddatabassökning, Vinnova Programkonferens 2004
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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
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MPEG-7 database of 5466 images50 standard queriesQuality measure
xperiments: Image database
Query image
Ground truth images
Reiner Lenz, Intelligent Bilddatabassökning, Vinnova Programkonferens 2004
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xperiments
Reiner Lenz, Intelligent Bilddatabassökning, Vinnova Programkonferens 2004
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ngines
Currently we have 3 big search engines Linköping University Electronic Press
Search engine developed as part of L. V. Tran’s PhD thesisbased on 126604 images from Matton AB, StockholmOld Search Engine:
http://www.ep.liu.se/databases/cse-imgdbThesis: http://www.ep.liu.se/diss/science_technology/08/10Text-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
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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.
Reiner Lenz, Intelligent Bilddatabassökning, Vinnova Programkonferens 2004
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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
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obust region merging
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