Fuzzy relevance feedback in content-based 3D model retrieval

Relevance feedback is an iterative search technique to bridge the semantic gap between the high level user intention and low level data representation. This technique interactively determines a user's desired output or query concept by asking the user whether certain proposed 3D models are rele...

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Bibliographic Details
Published in2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery Vol. 2; pp. 565 - 568
Main Authors Zhi-yong Zhang, Jian-qiu Jin
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2010
Subjects
3D model retrieval
Accuracy
Biological system modeling
component
Computational modeling
Fuzzy interpretation
Retrieval Feedback
Shape
Solid modeling
Three dimensional displays
Training
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Summary:Relevance feedback is an iterative search technique to bridge the semantic gap between the high level user intention and low level data representation. This technique interactively determines a user's desired output or query concept by asking the user whether certain proposed 3D models are relevant or not. For a relevance feedback algorithm to be effective, it must grasp a user's query concept accurately and quickly. In this paper, we propose a framework called fuzzy relevance feedback in content-based 3D model retrieval systems. Fuzzy relevance feedback is to integrate the users' fuzzy interpretation of semantic content into the notion of relevance feedback. Experimental results show that this algorithm achieves higher search accuracy than traditional query refinement schemes.
ISBN:1424459311
9781424459315
DOI:10.1109/FSKD.2010.5569481