ISSN : 1796-2048
Volume : 4    Issue : 3    Date : June 2009

A Powerful Partial Relevance Feedback for 3D Model Retrieval
Baokun Hu, Yusheng Liu, Shuming Gao, Jing Hu, and Rui Sun
Page(s): 120-128
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Relevance feedback(RF) has been proved to be an effective way to improve the precision and recall
of 3D model retrieval. However, the existing RF approaches do not consider which local part of the
feedback example is similar or dissimilar with the query model although they recorded whether the
whole model is similar or not. In this study, a partial relevance feedback(PRF) method which
overcomes this deficiency is discussed . First, an improved silhouette based descriptor is proposed
to satisfy the PRF method. Second, a new mathematical model for partial relevance feedback is set
up and optimal solution is also given: a SVM based classifier is trained to classify the models; the
variables which have influence on similarity measurement are optimized to minimize the average
distance between the query model and the feedback examples, and then the similarities between
the query model and all the models in database are recalculated. At last, Some experiments are
given to illustrate the outperformance of the proposed method over the other methods.

Index Terms
3D model retrieval, Partial retrieval, Partial relevance feedback, relevance feedback, Image retrieval