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“Inside the Bible” Segmentation, annotation and retrieval for a new browsing experience

International Doctorate school on Information and Communication Technologies English for academic purposes I. “Inside the Bible” Segmentation, annotation and retrieval for a new browsing experience. Daniele Borghesani. Goals Text segmentation Picture segmentation Results Conclusions.

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“Inside the Bible” Segmentation, annotation and retrieval for a new browsing experience

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  1. International Doctorate school on Information and Communication Technologies English for academic purposes I “Inside the Bible”Segmentation, annotation and retrieval for a new browsing experience Daniele Borghesani

  2. Goals Text segmentation Picture segmentation Results Conclusions Overview

  3. Goals Text segmentation Picture segmentation Results Conclusions Overview

  4. Dataset description

  5. Dataset description • Holy Bible of Borsod’Este (1450-1471 d.C.) • Illuminated manuscript • A lot of illustrations (biblical episodes, animals, symbols, court life scenes…) • 1200+ high resolution pages

  6. Manualannotation

  7. CBIR User interface Images database Our project Preprocessing Texture analysis Annotation database Text Illustrations Illustrations classification Text recognition Feature annotation Decorated initials Decoration Picture

  8. Our project • Automatic analysis of Bible pages • Extraction of valuable pictures • Addition of translations, commentaries, references… • Finally, media station with an appealing user interface (museums) Obscura HP Multi-Touch Video Wall

  9. Goals Text segmentation Picture segmentation Results Conclusions Overview

  10. Text Segmentation • Block analysis with autocorrelation • Directional histogram • Sum of pixel along each direction • Modeling with mixtures of Von Mises distributions • Very good for handling of angular data • Compact representation (5 values for a mixture of two Von Mises distributions)

  11. Text Segmentation !Text Text

  12. Text Segmentation

  13. Goals Text segmentation Picture segmentation Results Conclusions Overview

  14. Picture Segmentation Preprocess to focus on most important blobs of pixels (1) Original image (2) Background suppression and Labeling (fast) (4) Blob filling (3) Morphology

  15. Picture Segmentation Block analysis • SVM learning with a training set of positive and negative samples ... ... Features: color (HSV and RGB histogram), texture (gradients), low frequency coefficients • SVM classification on the pages…

  16. Goals Text segmentation Picture segmentation Results Conclusions Overview

  17. Results

  18. Results

  19. Retrieval by similarity

  20. BrowsingwithSammonMapping

  21. Conclusions • We are studying a set of techniques in order to analyze the Holy Bible of Borsod’Este • Our goal is to produce a media station, available both locally (museums) and remotely (web app), to “touch” this untouchable masterpiece

  22. Thank you!

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