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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

SP-ASC – July, 2010

Visual Analysis ofImage Collections

Danilo Medeiros Eler


Visual analysis of image collections

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


Contents
Contents

  • Exploration of image collections

  • Approach to compare

    • Distance metrics

    • Feature vectors

  • New approach to feature space definition


Least squares projection lsp
Least Squares Projection (LSP)

(Paulovich et al, 2008)



Projection explorer pex framework
Projection Explorer (PEx) Framework

(Paulovich et al, 2007)


Projection explorer for images pex image
Projection Explorer for Images(PEx-Image)

(Eler et al, 2009)



Detailed inspection
Detailed Inspection

537 X-Ray images

112 classes

(ImageCLEF 2006)

Wavelet Features


Detailed inspection1
Detailed Inspection

537 X-Ray images

112 classes

(ImageCLEF 2006)

Wavelet Features


Detailed inspection zoom in
Detailed Inspection (zoom in)

537 X-Ray images

112 classes

(ImageCLEF 2006)

Wavelet Features



Pex image image as visual mark
PEx-Image – Image as Visual Mark

537 X-Ray images

112 classes

(ImageCLEF 2006)

Wavelet Features



Imageclef training data set 1
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


Imageclef training data set 11
ImageCLEF Training Data Set (1)

9000 X-Ray images

116 classes

(ImageCLEF 2006)

Wavelet Features


Imageclef training data set 2
ImageCLEF Training Data Set (2)

Class 108

Class 111

9000 X-Ray images

116 classes

(ImageCLEF 2006)

Wavelet Features


Images without class information
Images Without Class Information

537 X-Ray images

112 classes

(ImageCLEF 2006)

Wavelet Features


Images without class information1
Images Without Class Information

537 X-Ray images

112 classes

(ImageCLEF 2006)

Wavelet Features


Colors from nn classifier
Colors from NN Classifier

Neural Network Classifier

Neural Network

Training Data Set

Neural Network Classifier

Image Data set

Labeled Images

Labeled Images


Colors from nn classifier 1
Colors from NN Classifier (1)

ClassInformation

NN Information

537 X-Ray images

112 classes

(ImageCLEF 2006)

Wavelet Features


Colors from nn classifier 11
Colors from NN Classifier (1)

ClassInformation

NN Information


Colors from nn classifier 12
Colors from NN Classifier (1)

ClassInformation

NN Information




Comparison of distance metrics
Comparison of Distance Metrics

City Block

Cosine

Euclidean

512 MRI medical images

12 classes


Comparison of distance metrics1
Comparison of Distance Metrics

City Block

Cosine

Euclidean

512 MRI medical images

12 classes


Comparison of feature space 1
Comparison of Feature Space (1)

72 co-ocurrence

matrices

16 Gabor

Filters

Fourier, Mean

and Deviation

All combined

512 MRI medical images

12 classes


Comparison of feature space 11
Comparison of Feature Space (1)

72 co-ocurrence

matrices

16 Gabor

Filters

Fourier, Mean

and Deviation

All combined

512 MRI medical images

12 classes


Comparison of feature space 2
Comparison of Feature Space (2)

All combined

Wavelet Features

1000 X-Ray images from ImageCLEF

116 classes


Comparison of feature space 21
Comparison of Feature Space (2)

All combined

Wavelet Features

1000 X-Ray images from ImageCLEF

116 classes


Recent approach brandoli et al 2010
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


Recent approach brandoli et al 20101
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)


Recent approach brandoli et al 20102
Recent Approach (Brandoli et al, 2010)

Dataset: 70 texture images from Brodatz

Features: Gabor filters (4 orientations and 4 scales)

Silhouette: 0.676


Recent approach brandoli et al 20103
Recent Approach (Brandoli et al, 2010)

Dataset: 100 texture images from Brodatz

Features: Gabor filters (4 orientations and 4 scales)

Silhouette: 0.429



Recent approach brandoli et al 20105
Recent Approach (Brandoli et al, 2010)

Dataset: 70 texture images from Brodatz

Features: Gabor filters (90o orientation and 4 scales)

Silhouette: 0.474


Recent approach brandoli et al 20106
Recent Approach (Brandoli et al, 2010)

Dataset: 70 texture images from Brodatz

Features: Gabor filters (90o orientation and 4 scales)

Silhouette: 0.474


Recent approach brandoli et al 20107
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


Recent example
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


Texture methods kth tips database colored texture
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%


Color methods kth tips database colored texture
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%


Color methods kth tips database colored texture1
Color Methods – KTH-TIPS database (Colored Texture)

Feature: SIFT

Silhouette: -0.1025

K-NN: 92%


Color methods kth tips database colored texture2
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%


Conclusions
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


Thank you
Thank you

  • More information:

    • http://infoserver.lcad.icmc.usp.br

    • danilome@gmail.com


References
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.


References1
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