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