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Learning-Based Indexing of Works of Art

Learning-Based Indexing of Works of Art. Kurt Grieb. Presentation Overview. Research Divided into 2 parts Parallel Upgrade of ALIP Structure of Parallelization Results EMPEROR Database Tests Setup of Tests Results. Reasons for Parallelization.

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Learning-Based Indexing of Works of Art

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  1. Learning-Based Indexing of Works of Art Kurt Grieb

  2. Presentation Overview • Research Divided into 2 parts • Parallel Upgrade of ALIP • Structure of Parallelization • Results • EMPEROR Database Tests • Setup of Tests • Results

  3. Reasons for Parallelization • ALIP statistical computations are computationally expensive • Corel Image Library Comparison: • 15-20 Minutes • Unacceptable for Web and other applications

  4. Server . . . . Client 1 – 30 Client 31 – 60 Client 541-570 Client 571-600 Parallelization Concept • One server receives request, divides workload between the total number of clients.

  5. Parallelization Structure Request With URL Range of Concepts Server CLIENTS PERLGUI Best Fit Likelihoods

  6. Results

  7. Results • 600 concepts can now be computed in roughly 40 seconds over 30 processors. • Roughly ideal speedup • More processors on a smaller size reduces efficiency of speedup

  8. The EMPEROR Library • 1700 Images • Chinese Historical Images

  9. Set 1 Set 2 Best Sub Best Sub Worst Sub Worst Sub Random 1 Random 1 Random 2 Random 2 Size 3 Size 3 Size 6 Size 6 Size 9 Size 9 Size 12 Size 12 The Testing • 2 sets of tests (9 and 20 concepts) • 4 runs per set (best, worst, 2 random) • 4 sizes per run (3, 6, 9, 12)

  10. Motivation For Test Structure • Effects of more specific classes • Effects of different training classes • Determine reasonable training sizes

  11. Set 1 Total Percentages 0.8 0.7 0.6 0.5 % Correct Worst Case Random 2 0.4 Random 1 Best Case Random Generation 0.3 0.2 0.1 0 3 6 9 12 Sample Size Results

  12. Interesting Cases / Notable Trends • Set One vs. Set Two • The Black and White Sketches • General Trends vs. Specific Classes • Weak Classes • Misclassification of Similar Objects • Black & White Images vs. Text • All faces vs. Color/BW Faces • Faces and Upper Bodies

  13. The Black and White Sketches • Performed the best of all classes • Accuracies of 99% over all tests • Due to difference between this class and most other classes

  14. Interesting Cases / Notable Trends • The overall accuracy of all classes went up with more training • In certain classes, the accuracy went down as all concepts were trained with more imaging

  15. Weak Classes • In certain concepts a weak class outperformed other classes • Could be due to openness of concept spaces

  16. Misclassification of Similar Objects • Pictures with more than one concept in them sometimes can confuse ALIP

  17. Misclassification of Similar Objects

  18. Further Work • Overlapping of Concepts • 3-D representations of objects • Improved Accuracy of ALIP • Current Results are Promising

  19. ABSTRACT • Digital images are widely and readily in use. Text based indexing of these images is becoming tougher as the number of digital images grows. Therefore, Content Based Image Retrieval is becoming a more viable alternative because of the ability to automate this process. Dr. Wang’s Automatic Linguistic Indexing of Pictures shows great promise as a Contend Based Image Retrieval system. Our lab is looking to expand this indexing of pictures for artistic/historical purposes, which are harder to classify due certain characterizes of these pictures. Additionally, some upgrades need to be made to ALIP in order to convert it to a more user-friendly, mainstream program. I present the results of the upgrades to ALIP and the experiments conducted on a historic image database.

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