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A Sparse Texture Representation Using Affine-Invariant Regions

A Sparse Texture Representation Using Affine-Invariant Regions. Svetlana Lazebnik, Jean Ponce Beckman Institute University of Illinois, Urbana, USA. Cordelia Schmid INRIA Rh ô ne-Alpes Grenoble, France.

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A Sparse Texture Representation Using Affine-Invariant Regions

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  1. A Sparse Texture Representation Using Affine-Invariant Regions Svetlana Lazebnik, Jean PonceBeckman InstituteUniversity of Illinois, Urbana, USA Cordelia SchmidINRIA Rhône-AlpesGrenoble, France Supported in part by the UIUC Campus Research Board, the UIUC/CNRS Collaborative Research Agreement, the National Science Foundation under grant IRI-990709, and by the European project LAVA (IST-2001-34405).

  2. Goal Develop a texture representation invariant to: • viewpoint changes • non-rigid deformations

  3. Without spatial selection With spatial selection Our Approach • Affine-invariant regions: robustness against geometric transformations • A sparse representation: saliency, compactness

  4. Outline

  5. Affine Region Detectors Harris detector (H) Laplacian detector (L) [Lindeberg & Gårding 1997, Mikolajczyk & Schmid 2002]

  6. Affine Rectification Process Patch 1 Patch 2 Rectified patches (rotational ambiguity)

  7. Spin Images as Intensity Descriptors • Range spin images: Johnson & Hebert (1998) • Two-dimensional histogram: distance from center × intensity value

  8. Signatures and EMD • SignaturesS = {(m1 , w1) , … , (mk , wk)}mi — representative of ith clusterwi — weight (relative size) of ith cluster • Earth Mover’s Distance [Rubner, Tomasi & Guibas 1998] • Computed from ground distancesd(mi, m'j) • Can compare signatures of different sizes • Insensitive to the number of clusters

  9. Evaluation • Retrieval and classification • Two experiments: • Viewpoint-invariant texture recognition • Brodatz database

  10. Viewpoint-Invariant Texture Recognition Data set: 10 textures, 20 samples each

  11. Results • Retrieval evaluation strategy: Picard et al. 1993, Liu & Picard 1996, Xu et al. 2000 • Gabor-like filters: Schmid 2000

  12. ? Classification Results

  13. A Closer Look Problem: viewpoint- and lighting-dependent appearance changes

  14. A Closer Look Problem: viewpoint- and lighting-dependent appearance changes

  15. Brodatz Database Evaluation • 111 classes, 9 samples each • No affine invariance required • Shape channel:

  16. Retrieval Results Better results: Xu, Georgescu, Comaniciu & Meer (2000)

  17. Classification Results

  18. Summary • Sparse representation • Flexible approach to invariance • Spin images as intensity descriptors Future Work • Evaluate more detector types [Kadir & Brady 2001, Tuytelaars & Van Gool 2001] • Compare spin images with descriptors of similar dimensionality (e.g. SIFT) • Enhance representation with spatial relations • Learning from multi-texture images • Texture segmentation

  19. ICCV 2003 • Neighborhood statistics • Learning from multi-texture images • Texture segmentation

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