1 / 29

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

thanh
Download Presentation

Content Based Image Retrieval

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  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

More Related