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Recognition of textures and object classes

Recognition of textures and object classes. Introduction. Invariant local descriptors => robust recognition of specific objects or scenes Recognition of textures and object classes => description of intra-class variation, selection of discriminant features. texture recognition.

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Recognition of textures and object classes

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  1. Recognition oftextures and object classes

  2. Introduction • Invariant local descriptors => robust recognition of specific objects or scenes • Recognition of textures and object classes => description of intra-class variation, selection of discriminant features texture recognition car detection

  3. Overview • An affine-invariant texture recognition (CVPR’03) • A two-layer architecture for texture segmentation and recognition (ICCV’03) • Feature selection for object class recognition (ICCV’03)

  4. Affine-invariant texture recognition • Texture recognition under viewpoint changes and non-rigid transformations • Use of affine-invariant regions • invariance to viewpoint changes • spatial selection => more compact representation, reduction of redundancy in texton dictionary [A sparse texture representation using affine-invariant regions, S. Lazebnik, C. Schmid and J. Ponce, CVPR 2003]

  5. Overview of the approach

  6. Region extraction Harris detector Laplace detector

  7. Descriptors – Spin images

  8. Spatial selection clustering each pixel clustering selected pixels

  9. Signature and EMD • Hierarchical clustering => Signature : • Earth movers distance • robust distance, optimizes the flow between distributions • can match signatures of different size • not sensitive to the number of clusters S = { ( m1 , w1 ) , … , ( mk , wk ) } D( S , S’ ) = [i,jfij d( mi , m’j)] / [i,j fij]

  10. Database with viewpoint changes 20 samples of 10 different textures

  11. Results Spin images Gabor-like filters

  12. A two-layer architecture • Texture recognition + segmentation • Classification of individual regions + spatial layout [A generative architecture for semi-supervised texture recognition, S. Lazebnik, C. Schmid, J. Ponce, ICCV 2003]

  13. A two-layer architecture Modeling : • Distribution of the local descriptors (affine invariants) • Gaussian mixture model • estimation with EM, allows incorporating unsegmented images • Co-occurrence statistics of sub-class labels over affinely adapted neighborhoods Segmentation + Recognition : • Generative model for initial class probabilities • Co-occurrence statistics + relaxation to improve labels

  14. Texture Dataset – Training Images T5 (floor 2) T1 (brick) T2 (carpet) T3 (chair) T4 (floor 1) T6 (marble) T7 (wood)

  15. Effect of relaxation + co-occurrence Original image Top: before relaxation (indivual regions), bottom: after relaxation (co-occurrence)

  16. Recognition + Segmentation Examples

  17. Animal Dataset – Training Images • no manual segmentation, weakly supervised • 10 training images per animal (with background) • no purely negative images

  18. Recognition + Segmentation Examples

  19. Object class detection • Description of intra-class variations of object parts [Selection of scale inv. regions for object class recognition, G. Dorko and C. Schmid, ICCV’03]

  20. Object class detection • Description of intra-class variations of object parts • Selection of discrimiant features

  21. Outline of the approach

  22. Clustering of descriptors • Descriptors are labeled as positive/negative • Hierarchical clustering of the positive/negative set • Examples of positive clusters

  23. Clustering of descriptors • Descriptors are labeled as positive/negative • Hierarchical clustering of the positive/negative set • Examples of positive clusters

  24. Classification • Learn a separate classifier for each cluster • Classifier : Support Vector Machine • Select significant classifiers • Feature selection with likelihood ratio / mutual information

  25. Likelihood – mutual information Likelihood Mutual Information 5 10 25

  26. Summary - Approach • Automatic construction of object part classifiers • scale and rotation invariant • no normalization/alignment of the training and test images • Selection of discriminant features • interest points, clustering • feature selection with likelihood or mutual information • Comparison of two feature selection methods • likelihood: more discriminant but very specific • mutual Information: discriminant but not too specific

  27. Material • Powerpoint presention and papers will be available at http://www.inrialpes.fr/movi/people/Schmid/cvpr-tutorial03

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