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Spatial Pyramid Co-occurrence for Image Classification

Spatial Pyramid Co-occurrence for Image Classification. Presenter : Han-Mu Park. Spatial Pyramid Co-occurrence for Image Classification, ICCV 2011. Contents. Introduction Coding methods Proposed method Experimental results Conclusion References.

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Spatial Pyramid Co-occurrence for Image Classification

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  1. Spatial Pyramid Co-occurrence for Image Classification Presenter : Han-Mu Park

  2. Spatial Pyramid Co-occurrence for Image Classification, ICCV 2011 Contents • Introduction • Coding methods • Proposed method • Experimental results • Conclusion • References

  3. Spatial Pyramid Co-occurrence for Image Classification, ICCV 2011 Introduction • Bag-of-Words (BoW) model • An image is represented as a collection of visual words. • Generally, to represent the collection, histogram of words form is used.

  4. Spatial Pyramid Co-occurrence for Image Classification, ICCV 2011 Introduction • Spatial Pyramid Matching (SPM) • Original SPM partitions an image into a sequence of spatial grids at resolutions . • The grid at level has cells along each dimension for a total of cells. • Some variations used different shaped partitions. • Overlapped partitions • Vertically divided parts (ex) 3x1, 3x2) Example of a tree-level SPM Example of overlapped partitions [J.WU2012]

  5. Spatial Pyramid Co-occurrence for Image Classification, ICCV 2011 Introduction • Testing images • SPM can extract the characteristics of “car”.

  6. Spatial Pyramid Co-occurrence for Image Classification, ICCV 2011 Introduction • Testing images • SPM can extract the characteristics of “car”…?

  7. Spatial Pyramid Co-occurrence for Image Classification, ICCV 2011 Introduction • Testing images • SPM can extract the characteristics of “car”…?

  8. Spatial Pyramid Co-occurrence for Image Classification, ICCV 2011 Introduction • Correlation between codewords • Correlogram extracts frequent pattern information from the co-occurrence of codewordsin the local region. • Correlation between codewords is extracted from correlogram by mining the primitive patterns. Example of local regions [S.Savarese2006] (a) Image (b) codeword image (c) correlogram V(1,2) [S.Savarese2006]

  9. Spatial Pyramid Co-occurrence for Image Classification, ICCV 2011 Introduction • Motivation • Both of spatial characteristics, absolute and relative, have to be considered. • Various local spatial arrangements should be handled. • The method can be easily combined with conventional framework.

  10. Spatial Pyramid Co-occurrence for Image Classification, ICCV 2011 Proposed method • Bag-of-Visual-Words (BOVW) representation • The non-spatial BOVW representation simply records the visual word occurrences in an image. • It is typically represented as a histogram • : # of occurrences of visual word • BOVW kernel (intersection kernel)

  11. Spatial Pyramid Co-occurrence for Image Classification, ICCV 2011 Proposed method • Spatial Pyramid representation • The spatial pyramid representation partitions an image into a sequence of spatial grids at resolutions . • Such that the grid at level has cells. • A Spatial Pyramid Match Kernel (SPMK)

  12. Spatial Pyramid Co-occurrence for Image Classification, ICCV 2011 Proposed method • Spatial Co-occurrence representation • A binary spatial predicate where is defined. • The Visual Word Co-occurrence Matrix (VWCM) is defined as a count of the number of times two visual words satisfy the spatial predicate. • Spatial Co-occurrence Kernel (SCK)

  13. Spatial Pyramid Co-occurrence for Image Classification, ICCV 2011 Proposed method • Spatial predicates • Proximity • Orientation

  14. Spatial Pyramid Co-occurrence for Image Classification, ICCV 2011 Proposed method • Spatial predicates

  15. Spatial Pyramid Co-occurrence for Image Classification, ICCV 2011 Proposed method • Combining multiple spatial predicates • Multiple binary spatial predicates can be easily combined. • The combined SCK is simply computed as the sum of the individual SCKs.

  16. Spatial Pyramid Co-occurrence for Image Classification, ICCV 2011 Proposed method • Spatial Pyramid Co-occurrence representation • A spatial predicate is computed for each cell • Spatial Pyramid Co-occurrence Kernel (SPCK) , where

  17. Spatial Pyramid Co-occurrence for Image Classification, ICCV 2011 Proposed method • Extended SPCK • The extended SPCK merges SPCK and non-spatial BOVW or SPMK. • Extended SPCK+ (SPCK & non-spatial BOVW) • Extended SPCK++ (SPCK & SPMK)

  18. Spatial Pyramid Co-occurrence for Image Classification, ICCV 2011 Experimental results • Evaluation data sets • Land-Use data set • Aerial orthoimagery • 256 x 256 pixel • 21 classes • Agricultural, airplane, baseball diamond, beach, …

  19. Spatial Pyramid Co-occurrence for Image Classification, ICCV 2011 Experimental results • Evaluation data sets • GRAZ-01 data set • High intra-class variation • 640 x 480 pixel • 3 classes • Bike, Person, Background

  20. Spatial Pyramid Co-occurrence for Image Classification, ICCV 2011 Experimental results • Evaluation data sets • 15 scene data set • Images in the same class have similar composition. • 300 x 300 pixel • 15 classes • Bedroom, Kitchen, coast, city, forest, …

  21. Spatial Pyramid Co-occurrence for Image Classification, ICCV 2011 Experimental results • Land-Use data set

  22. Spatial Pyramid Co-occurrence for Image Classification, ICCV 2011 Experimental results • Land-Use data set

  23. Spatial Pyramid Co-occurrence for Image Classification, ICCV 2011 Experimental results • Land-Use data set

  24. Spatial Pyramid Co-occurrence for Image Classification, ICCV 2011 Experimental results • Graz-01 data set • 15 Scene data set

  25. Spatial Pyramid Co-occurrence for Image Classification, ICCV 2011 Conclusion • Conclusion • In this paper, Spatial Pyramid Co-occurrence Kernel (SPCK) is proposed. • The proposed method includes absolute and relative spatial information of codewords • The proposed method shows better performance than non-spatial BoVW and SPMK framework

  26. Spatial Pyramid Co-occurrence for Image Classification, ICCV 2011 References [1] Y. Yang and S. Newsam, “Spatial Pyramid Co-occurrence for Image Classification,” ICCV 2011. [2] J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, Y. Gong, “Locality-constrained Linear Coding for Image Classification,” CVPR 2010. [3] J. Wu, M. Rehg, “CENTRIST: A Visual Descriptor for Scene Categorization,” PAMI 2011. [4] S. Savarese, J. Winn, A. Criminisi, “Discriminative Object Class Models of Appearance and Shape by Correlations,” CVPR 2006.

  27. Thank you!

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