Shapeme histogram projection and matching for partial object recognition

• Published in: IEEE transactions on pattern analysis and machine intelligence 2006-04, Vol.28 (4), p.568-577
• Main Authors: Ying Shan, Sawhney, H.S., Matei, B., Kumar, R.
• Format: Article
• Language: English
• Subjects: Algorithms; Applied sciences; Artificial Intelligence; Bayesian analysis; Bayesian methods; Computer science; control theory; systems; Computer Simulation; Constraint optimization; Exact sciences and technology; feature saliency; Gibbs sampling; Histograms; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Imaging, Three-Dimensional - methods; Information Storage and Retrieval - methods; Information systems. Data bases; Layout; Matching; Memory organisation. Data processing; Models, Biological; Models, Statistical; Object recognition; Pattern Recognition, Automated - methods; Pattern recognition. Digital image processing. Computational geometry; Projection; Prototypes; Query processing; Recognition; Reproducibility of Results; Sampling methods; Sensitivity and Specificity; Shape measurement; Shapeme histogram; Software; Spatial databases; spin image; Studies; Subspace constraints
• ISSN: 0162-8828; 1939-3539
• DOI: 10.1109/TPAMI.2006.83

Histograms of shape signature or prototypical shapes, called shapemes, have been used effectively in previous work for 2D/3D shape matching and recognition. We extend the idea of shapeme histogram to recognize partially observed query objects from a database of complete model objects. We propose representing each model object as a collection of shapeme histograms and match the query histogram to this representation in two steps: 1) compute a constrained projection of the query histogram onto the subspace spanned by all the shapeme histograms of the model and 2) compute a match measure between the query histogram and the projection. The first step is formulated as a constrained optimization problem that is solved by a sampling algorithm. The second step is formulated under a Bayesian framework, where an implicit feature selection process is conducted to improve the discrimination capability of shapeme histograms. Results of matching partially viewed range objects with a 243 model database demonstrate better performance than the original shapeme histogram matching algorithm and other approaches.