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Content Based Image Retrieval

Content Based Image Retrieval. Romit Das · Ryan Scotka. GIS Problems. Search based on filename Verbatim match Noun replacement Potential for Abuse (Google Hack). Possible Solutions. Metadata Standards Re-index existing images Manual Classification Time Content-based Classification.

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Content Based Image Retrieval

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  1. Content Based Image Retrieval Romit Das · Ryan Scotka

  2. GIS Problems • Search based on filename • Verbatim match • Noun replacement • Potential for Abuse (Google Hack)

  3. Possible Solutions • Metadata • Standards • Re-index existing images • Manual Classification • Time • Content-based Classification

  4. CBIR – Training • Choose features to distinguish images. • Extract said features. • Apply statistical method to model features. • Categorize based on textual description.

  5. Example Dimensions Color Frequencies Spatial Distribution 200 x 200 + Mostly flesh tones + Flesh tones concentrated in the center = baby

  6. Author’s Feature Set • Feature Set (6 dimensions): • Color averages (LUV) • High-frequency energy bands • “Effectively discern local texture” • Wavelet transform on 4x4 blocks • Use HL, LH, and HH “high energy bands” • Use the LL for lower resolution analysis

  7. Author’s Implementation • Statistical Modeling • Use machine learning to build concepts Concept = Paris Training Set =

  8. Markov Models • Take known facts • Deduce hidden/unknown data

  9. Markov Model Example • Given: • Queues of people, shelves, price labels, disgruntled workers • Possible Results: • Post office • Supermarket • Record Store

  10. Markov Model Example • Given: • Queues of people, shelves, price labels, disgruntled workers, food products • Possible Results: • Post office • Supermarket • Record Store

  11. Ninja Model Person, outdoors

  12. Ninja Model People, ninjas, outdoor

  13. Ninja Model People, ninjas, weapons, outdoors

  14. Ninja Markov Model Person, outdoors People, ninjas, outdoors People, ninjas, outdoors weapons, class photo

  15. Creating Concepts • Training Concept • Created from hand-picked images • Must choose statistically significant training size • Resulting Concept • Used in automatic cataloging of future images

  16. People, ninjas, outdoors weapons, class photo Observations • Images are associated with multiple concepts. • Not foolproof • Example:

  17. Advantages • Automatic categorization

  18. Disadvantages • False positives • Concepts may require a vast amount of images • Increases training time • Dissimilar images needed for training of a concept

  19. Future Additions • Further refinement of conflicting semantics • Weights assigned to classifications

  20. Our Implementation • Perform classification with alternate learners (Weka)

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