Title: A semantic recommender algorithm for 3D model retrieval based on deep belief networks

Authors: Li Chen; Hong Liu; Philip Moore

Addresses: School of Information Science and Engineering, Shandong Normal University, Jinan, China ' Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology, School of Information Science and Engineering, Shandong Normal University, Jinan, China ' School of Information Science and Engineering, Shandong Normal University, Jinan, China

Abstract: Interest in 3D modelling is growing, however, the retrieval results achieved for semantic-based 3D model retrieval systems have been disappointing. In this paper, we propose a novel semantic recommendation algorithm based on a deep belief network (DBN-SRA) to implement semantic retrieval with potential semantic correlations [between models] being achieved using deep learning from known model samples. The algorithm uses the feature correlation [between the models] as the conditions to enable semantic matching of 3D models to obtain the final recommended retrieval result. Our proposed approach has been shown to improve the effectiveness of 3D model retrieval in terms of both retrieval time and importantly accuracy. Additionally, our study and our reported results suggest that our posited approach will generalise to recommender systems in other domains which are characterised by multiple feature relationships.

Keywords: deep belief network; DBN; 3D model retrieval; recommender algorithm; cluster analysis.

DOI: 10.1504/IJCSE.2019.099643

International Journal of Computational Science and Engineering, 2019 Vol.19 No.1, pp.93 - 103

Received: 03 Jun 2016
Accepted: 04 Nov 2016

Published online: 02 May 2019 *

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