Feature Combination and Relevance Feedback for 3D Model Retrieval

Retrieval of 3D models have attracted much research interest, and many types of shape features have been proposed. In this paper, we describe a novel approach of combining the feature types for 3D model retrieval and relevance feedback processing.Our approach performs query processing using pre-comp...

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Hauptverfasser:	Atmosukarto, I., Wee Kheng Leow, Zhiyong Huang
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Computer science Drives Feature extraction Feedback Histograms Image retrieval Performance evaluation Query processing Shape Testing
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creator	Atmosukarto, I. Wee Kheng Leow Zhiyong Huang
description	Retrieval of 3D models have attracted much research interest, and many types of shape features have been proposed. In this paper, we describe a novel approach of combining the feature types for 3D model retrieval and relevance feedback processing.Our approach performs query processing using pre-computed pairwise distances between objects measured according to various feature types. Experimental tests show that this approach performs better than retrieval by individual feature type.
doi_str_mv	10.1109/MMMC.2005.39
format	Conference Proceeding
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source	IEEE Electronic Library (IEL) Conference Proceedings
subjects	Computer science Drives Feature extraction Feedback Histograms Image retrieval Performance evaluation Query processing Shape Testing
title	Feature Combination and Relevance Feedback for 3D Model Retrieval
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