IKAT Promovendidag

Shape matching for classification of historical glass: 

Shape matching for classification of historical glass Laurens van der Maaten IKAT-Promovendidag ‘05

Introduction: 

Introduction Classification of archaeological artifacts Now performed manually by experts Expert compares artifact with objects from reference collection Reference collections consist of drawings in books Thus: slow, subjective, and error-prone process

Example: 

Example An archaeological artifact:

Example: 

Example And its corresponding drawing: 

The task: 

The task Given an artifact photograph Find the most ‘alike’ drawings Speeds up the classification process Can give archaeological experts new insights

The problem: 

The problem Drawings contain no color information Drawings contain only very abstract texture representation Texture hard to extract from glass photographs Thus: only outer shape information useful Shape matching

Shape matching: 

Shape matching Several approaches: Shape contexts (Belongie, 2000) Curvature scale spaces (Mokhtarian, 1996) Turning functions (Veltkamp, 2003) Dynamic programming (Petrakis, 2002) Moment invariants (Hu, 1962) Hausdorff, Procrustes, etc. MPEG-7 standard: CSS

Shape matching: 

Shape matching We compared various approaches: Shape contexts Curvature scale space Desired properties of approach: Invariant to scale, translation, and rotation Robust to distortions due to Broken artifacts 3D rotations Drawing interpretations

Broken artifacts: 

Broken artifacts 

Or worse…: 

Or worse…

3D rotations: 

3D rotations 

Shape contexts: 

Shape contexts Sample points from outer contour For all points: Determine angle (relative to baseline) and distance to all other points in log-polar space All resulting histograms form the shape description

Shape contexts: 

Shape contexts Matching using startpoint invariant k-NN classifier (using χ2-distance) Startpoint invariance obtained by circular shifting one of the histograms

Curvature scale space: 

Curvature scale space Determine positions of zero-crossings of curvature for an ‘evolving’ shape contour Curvature is a function that is 1 for a straight line, and 1 / r for a circle with radius r Shape evolution: convolve coordinates with 1D Gaussian kernel with increasing variance

Shape evolution: 

Shape evolution Evolving shape with curvature zero-crossings:

Curvature scale space: 

Curvature scale space CSS image: Align CSS by aligning global maximum Sum distances between main peaks

Results: 

Results Low identification performance (expected) due to difficult dataset

Results: 

Results We examined various variations, such as quantization in shape context space, etc. Best performance: 33% for hitlist size 10 However: For ‘good’ artifacts results are encouraging Shape analysis on reference collection allows for making ‘shape maps’ (using MDS) This allows for archaeologists to create new typologies (since archaeological typologies are not ‘fixed’)

Example query: 

Example query 

Conclusions: 

Conclusions Matching glass artifacts with drawings is a difficult problem Shape context matching outperforms (MPEG-7 standard) CSS matching Allows for shape analysis of reference collection Future research should focus extracting texture from photographs (e.g., Gabor)

Questions: 

Questions ?