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Visual Analysis of Image Collections

SP-ASC – July, 2010. Visual Analysis of Image Collections. Danilo Medeiros Eler. SP-ASC – July, 2010. Visual Analysis of Image Collections. Danilo Medeiros Eler Marcel Yugo Nakazaki Fernando Vieira Paulovich Davi Pereira Santos Gabriel Andery Bruno Brandoli

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Visual Analysis of Image Collections

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  1. SP-ASC – July, 2010 Visual Analysis ofImage Collections Danilo Medeiros Eler

  2. SP-ASC – July, 2010 Visual Analysis ofImage Collections Danilo Medeiros Eler Marcel Yugo Nakazaki Fernando Vieira Paulovich Davi Pereira Santos Gabriel Andery Bruno Brandoli Maria Cristina Ferreira de Oliveira João do Espírito Santo Batista Neto Rosane Minghim

  3. Contents • Exploration of image collections • Approach to compare • Distance metrics • Feature vectors • New approach to feature space definition

  4. Least Squares Projection (LSP) (Paulovich et al, 2008)

  5. Neighbor-Joining (NJ) Similarity Tree (Cuadros et al, 2007)

  6. Projection Explorer (PEx) Framework (Paulovich et al, 2007)

  7. Projection Explorer for Images(PEx-Image) (Eler et al, 2009)

  8. PEx-Image – Sample Content

  9. Detailed Inspection 537 X-Ray images 112 classes (ImageCLEF 2006) Wavelet Features

  10. Detailed Inspection 537 X-Ray images 112 classes (ImageCLEF 2006) Wavelet Features

  11. Detailed Inspection (zoom in) 537 X-Ray images 112 classes (ImageCLEF 2006) Wavelet Features

  12. PEx-Image – Group Content

  13. PEx-Image – Image as Visual Mark 537 X-Ray images 112 classes (ImageCLEF 2006) Wavelet Features

  14. video_Interaction.avi

  15. ImageCLEF Training Data Set (1) 9000 X-Ray images 116 classes (ImageCLEF 2006) 9000 X-Ray images 116 classes (ImageCLEF 2006) Wavelet Features Wavelet Features

  16. ImageCLEF Training Data Set (1) 9000 X-Ray images 116 classes (ImageCLEF 2006) Wavelet Features

  17. ImageCLEF Training Data Set (2) Class 108 Class 111 9000 X-Ray images 116 classes (ImageCLEF 2006) Wavelet Features

  18. Images Without Class Information 537 X-Ray images 112 classes (ImageCLEF 2006) Wavelet Features

  19. Images Without Class Information 537 X-Ray images 112 classes (ImageCLEF 2006) Wavelet Features

  20. Colors from NN Classifier Neural Network Classifier Neural Network Training Data Set Neural Network Classifier Image Data set Labeled Images Labeled Images

  21. Colors from NN Classifier (1) ClassInformation NN Information 537 X-Ray images 112 classes (ImageCLEF 2006) Wavelet Features

  22. Colors from NN Classifier (1) ClassInformation NN Information

  23. Colors from NN Classifier (1) ClassInformation NN Information

  24. PEx-Image – Coordination

  25. PEx-Image – Coordination

  26. Comparison of Distance Metrics City Block Cosine Euclidean 512 MRI medical images 12 classes

  27. Comparison of Distance Metrics City Block Cosine Euclidean 512 MRI medical images 12 classes

  28. Comparison of Feature Space (1) 72 co-ocurrence matrices 16 Gabor Filters Fourier, Mean and Deviation All combined 512 MRI medical images 12 classes

  29. Comparison of Feature Space (1) 72 co-ocurrence matrices 16 Gabor Filters Fourier, Mean and Deviation All combined 512 MRI medical images 12 classes

  30. Comparison of Feature Space (2) All combined Wavelet Features 1000 X-Ray images from ImageCLEF 116 classes

  31. Comparison of Feature Space (2) All combined Wavelet Features 1000 X-Ray images from ImageCLEF 116 classes

  32. Recent Approach (Brandoli et al, 2010) • Main Goals • Visual framework which help users to better “understand” different sets of features • A method to objectively evaluate the quality of projections

  33. Recent Approach (Brandoli et al, 2010) The silhouette can vary between -1 <= S <= 1 Larger values indicate better cohesion and separation between clusters (Brandoli et al, 2010)

  34. Recent Approach (Brandoli et al, 2010) Dataset: 70 texture images from Brodatz Features: Gabor filters (4 orientations and 4 scales) Silhouette: 0.676

  35. Recent Approach (Brandoli et al, 2010) Dataset: 100 texture images from Brodatz Features: Gabor filters (4 orientations and 4 scales) Silhouette: 0.429

  36. Recent Approach (Brandoli et al, 2010) Zoom in

  37. Recent Approach (Brandoli et al, 2010) Dataset: 70 texture images from Brodatz Features: Gabor filters (90o orientation and 4 scales) Silhouette: 0.474

  38. Recent Approach (Brandoli et al, 2010) Dataset: 70 texture images from Brodatz Features: Gabor filters (90o orientation and 4 scales) Silhouette: 0.474

  39. Recent Approach (Brandoli et al, 2010) Dataset: 70 texture images from Brodatz Features: Co-occurrence Matrix (5 measures, 5 distances and 4 directions) Silhouette: 0.583

  40. Recent Example • KTH-TIPS database • 10 colorful texture classes • 9 different scales • 3 illumination directions and 3 poses • 9 images per scale • Texture methods • Gabor Filtes • Co-Occurrence Matrix • Color methods • Color Moment Invariants • RGB Histogram • SIFT

  41. Texture Methods – KTH-TIPS database (Colored Texture) Feature: Gabor Silhouette: -0.2535 K-NN: 83% Feature: Gabor Silhouette: -0.2535 K-NN: 83% Feature: Co-occurrence Matrix Silhouette: -0.3727 K-NN: 70%

  42. Color Methods – KTH-TIPS database (Colored Texture) Feature: Color Moment Invariants Silhouette: -0.2835 K-NN: 78% Feature: RGB Histogram Silhouette: -0.1845 K-NN: 91%

  43. Color Methods – KTH-TIPS database (Colored Texture) Feature: SIFT Silhouette: -0.1025 K-NN: 92%

  44. Color Methods – KTH-TIPS database (Colored Texture) Feature: All Previous Combined Silhouette: -0.2547 K-NN: 84% Feature: PCA Reduction to 10 dimensions Silhouette: 0.1290 K-NN: 98%

  45. Conclusions • PEx-Image: a set of tools and a novel approach to • Map an image data set onto 2D space • Make data analysis and exploration more effective • Provide evaluation of • Similarity measures • Feature spaces • Feature selection strategies • Recent Approach (Brandoli et al, 2010) • Guidance to understand and define a set of features that properly represents an image dataset

  46. Thank you • More information: • http://infoserver.lcad.icmc.usp.br • danilome@gmail.com

  47. References • Eler, D.; Nakazaki, M.; Paulovich, F.; Santos, D.; Andery, G.; Oliveira, M.; Batista, J.; Minghim, R. Visual analysis of image collections. The Visual Computer, v. 25, n. 10, p. 923–937, 2009. • Eler, D. M.; Nakazaki, M. Y.; Paulovich, F. V.; Santos, D. P.; Oliveira, M. C. F.; Batista, J.; Minghim, R. Multidimensional visualization to support analysis of image collections. In: Proceedings of the XXI Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI 2008), Campo Grande, Brazil: IEEE Computer Society, 2008, p. 289–296. • Eler, D. M.; Paulovich, F. V.; Oliveira, M. C. F. d.; Minghim, R. Coordinated and multiple views for visualizing text collections. In: IV ’08: Proceedings of the 12th International Conference Information Visualisation, Washington, DC, USA: IEEE Computer Society, 2008, p. 246–251. • Eler, D. M.; Paulovich, F. V.; Oliveira, M. C. F. d.; Minghim, R. Topic-based coordination for visual analysis of evolving document collections. In: IV ’09: Proceedings of the 13th International Conference Information Visualisation, Washington, DC, USA: IEEE Computer Society, 2009, p. 149–155. • Paulovich, F. V.; Eler, D. M.; Poco, J.; Nonato, L. G.; Botha, C. P.; Minghim, R. A fast projection technique and its applications to visualization of large data sets. Technical Report 349, Instituto de Ciências Matemáticas e de Computação – Universidade de São Paulo, 2010.

  48. References • PAULOVICH, F. V.; OLIVEIRA, M. C. F.; MINGHIM, R. The Projection Explorer: A flexible tool for projection-based multidimensional visualization. In: Proceedings of the XX Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI ’07), Washington, DC, USA: IEEE Computer Society, 2007, p. 27–36 • CUADROS, A. M.; PAULOVICH, F. V.; MINGHIM, R.; TELLES, G. P. Point placement by phylogenetic trees and its application for visual analysis of document collections. In: IEEE Symposium on Visual Analytics Science and Technology 2007, Sacramento, CA, USA, 2007, p. 99–106 • Brandoli, B.; Eler, D. M.; Paulovich, F. V.; Minghim, R.; Batista, J. Visual Data Exploration to Feature Space Definition. In: Proceedings of the XXIII Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI 2010) – To Appear – Gramado, Brazil: IEEE Computer Society, 2010

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