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A Sparse Texture A Sparse Texture Representation Using Representation Using Affine-Invariant RegionsAffine-Invariant Regions
Svetlana Lazebnik, Jean PonceSvetlana Lazebnik, Jean PonceBeckman InstituteUniversity of Illinois, Urbana, USA
Cordelia SchmidCordelia 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).
GoalGoalDevelop a texture representation invariant to:
– viewpoint changes
– non-rigid deformations
Our ApproachOur Approach• Affine-invariant regions: robustness against geometric
transformations• A sparse representation: saliency, compactness
Without spatial selection With spatial selection
OutlineOutline
Affine Region DetectorsAffine Region DetectorsHarris detector (H) Laplacian detector (L)
[Lindeberg & Gårding 1997, Mikolajczyk & Schmid 2002]
Affine Rectification ProcessAffine Rectification Process
Patch 2Patch 1
Rectified patches (rotational ambiguity)
Spin Images as Intensity DescriptorsSpin Images as Intensity Descriptors
• Range spin images: Johnson & Hebert (1998)• Two-dimensional histogram:
distance from center × intensity value
Signatures and EMDSignatures and EMD
• Signatures
S = {(m1 , w1) , … , (mk , wk)} mi — representative of ith cluster wi — weight (relative size) of ith cluster
• Earth Mover’s Distance [Rubner, Tomasi & Guibas 1998] – Computed from ground distances d(mi , m'j)
– Can compare signatures of different sizes – Insensitive to the number of clusters
EvaluationEvaluation
• Retrieval and classification• Two experiments:
– Viewpoint-invariant texture recognition– Brodatz database
Viewpoint-Invariant Texture RecognitionViewpoint-Invariant Texture RecognitionData set: 10 textures, 20 samples each
ResultsResults
• Retrieval evaluation strategy: Picard et al. 1993, Liu & Picard 1996, Xu et al. 2000
• Gabor-like filters: Schmid 2000
Classification ResultsClassification Results
??
A Closer LookA Closer LookProblem: viewpoint- and lighting-dependent appearance changes
A Closer LookA Closer LookProblem: viewpoint- and lighting-dependent appearance changes
Brodatz Database EvaluationBrodatz Database Evaluation
• 111 classes, 9 samples each• No affine invariance required• Shape channel:
Retrieval ResultsRetrieval Results
Better results: Xu, Georgescu, Comaniciu & Meer (2000)
Classification ResultsClassification Results
SummarySummary• Sparse representation• Flexible approach to invariance• Spin images as intensity descriptors
Future WorkFuture 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
ICCV 2003ICCV 2003• Neighborhood statistics • Learning from multi-texture images• Texture segmentation