A Probability-Based Unified 3D Shape Search

  • Suyu Hou
  • Karthik Ramani
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4306)


We present a probability-based unified search framework composed of semi-supervised semantic clustering and then a constraint-based shape matching. Given a query, we propose to use an ensemble of classifiers to estimate the likelihood of the query belonging to each category by exploring the strengths from individual classifiers. Three descriptors driven by Multilevel-Detail shape descriptions have been used to generate the classifier independently. A weighted linear combination rule, called MCE (Minimum Classification Error), is adapted to support high-quality downstream application of the unified search. Experiments are conducted to evaluate the proposed framework using the Engineering Shape Benchmark database. The results have shown that search effectiveness is significantly improved by enforcing the probability-based semantic constraints to shape-based similarity retrieval.


Combination Rule Precision Recall Curve Classifier Combination Combine Classifier Semantic Cluster 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Suyu Hou
    • 1
  • Karthik Ramani
    • 1
  1. 1.Purdue Research and Education Center of Information Systems in Engineering (PRECISE), School of Mechanical EngineeringPurdue UniversityWest LafayetteU.S.A.

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