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Content Based Image Retrieval

Content Based Image Retrieval. Miguel Arevalillo-Herráez. Contents. Introduction Information retrieval Image retrieval CBIR Approaches Combining similarity measures Full CBIR systems Possible extensions to 3D Results and Conclusions. Concepts. Information retrieval

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Content Based Image Retrieval

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  1. Content Based Image Retrieval Miguel Arevalillo-Herráez

  2. Contents • Introduction • Information retrieval • Image retrieval • CBIR • Approaches • Combining similarity measures • Full CBIR systems • Possible extensions to 3D • Results and Conclusions

  3. Concepts • Information retrieval • Objects are documents • Concept of a query • Image retrieval • Objects are images • Concept of a query • Content Based Image retrieval

  4. Common setup for CBIR

  5. The method • How do we judge how similar two images are?

  6. The method • How do we judge how similar two images are? - feature vectors

  7. The method • How do we judge how similar two images are? - feature vectors • How do we compare these vectors?

  8. The method • How do we judge how similar two images are? - feature vectors • How do we compare these vectors? • distance funcions defined over the feature space.

  9. The method • How do we judge how similar two images are? - feature vectors • How do we compare these vectors? • distance funcions defined over the feature space. • How are these distances combined to yield a composite similarity value?

  10. The method • How do we judge how similar two images are? - feature vectors • How do we compare these vectors? • distance funcions defined over the feature space. • How are these distances combined to yield a composite similarity value? • Normalization and combination

  11. The method • How do we judge how similar two images are? - feature vectors • How do we compare these vectors? • distance funcions defined over the feature space. • How are these distances combined to yield a composite similarity value? • Normalization and combination • How are multiple selections combined?

  12. The method • How do we judge how similar two images are? - feature vectors • How do we compare these vectors? • distance funcions defined over the feature space. • How are these distances combined to yield a composite similarity value? • Normalization and combination • How are multiple selections combined? • Multiple selection approaches

  13. The method • How do we judge how similar two images are? - feature vectors • How do we compare these vectors? • distance funcions defined over the feature space. • How are these distances combined to yield a composite similarity value? • Normalization and combination • How are multiple selections combined? • Multiple selection approaches

  14. Normalization and Combination Rules • Classical normalization rules: • Gaussian • Linear • Classical combination rules: • Sum • Product • Linear combination

  15. Probabilistic Approach • For each distance function we estimate the probability that the user considers that two images are similar, for every possible distance value: p(similar | di) • This is performed from a training set

  16. Probabilistic Approach • For each distance function we estimate the probability that the user considers that two images are similar, for every possible distance value: p(similar | di) • This is performed from a training set • p(similar | d1, d2, d3,…,dn)  p(similar | d1) x p(similar | d2) x p(similar | d3) x … x p(similar | dn)

  17. Handling Multiple Selections • Classical Approaches: • Query point movement and axis re-weighting • Support Vector Machines • Probabilistic and Regression Approaches • Other interesting approaches: • SOM based • Nearest neighbour

  18. Fuzzy Approach - Concepts • Need to deal with uncertainty of the data • Classical set: • Elements are or are not in the set • Fuzzy set: • Elements have a degree of membership to the set

  19. Fuzzy approach • Assumes an underlying search model • Any image of interest should be perceptually similar to each of the pictures in the set Positive in at least kpos characteristics. • Any image of interest should be perceptually different from each of the pictures in the set Negative in at least knegcharacteristics.

  20. Fuzzy approach • Every iteration the user is more exigent: Kpos and Kneg vary at each iteration

  21. Fuzzy Approach

  22. Genetic Approach • An evolutionary algorithm attempts to solve a problem applying Darwin’s basic principles of evolution on a population of trial solutions to a problem, called individuals.

  23. Genetic Approach

  24. Genetic Approach • Key issues: • Existence of fitness function • Relevance feedback defines population and fitness • Maintaining consistency • How do we judge next generation?

  25. Genetic Approach

  26. Genetic Approach

  27. Possible extensions to 3D • How do we judge how similar two images are? - feature vectors • How do we compare these vectors? • distance funcions defined over the feature space. • How are these distances combined to yield a composite similarity value? • Normalization and combination • How are multiple selections combined? • Multiple selection approaches

  28. Results and Conclusions • Introduction to the CBIR problem • Feature extraction • Definition of distance funcions normalization and combination • Handling multiple selections • Posible extensions to 3D

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