Abstract
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.
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