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Proceedings Paper

A diagram retrieval method with multi-label learning
Author(s): Songping Fu; Xiaoqing Lu; Lu Liu; Jingwei Qu; Zhi Tang
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Paper Abstract

In recent years, the retrieval of plane geometry figures (PGFs) has attracted increasing attention in the fields of mathematics education and computer science. However, the high cost of matching complex PGF features leads to the low efficiency of most retrieval systems. This paper proposes an indirect classification method based on multi-label learning, which improves retrieval efficiency by reducing the scope of compare operation from the whole database to small candidate groups. Label correlations among PGFs are taken into account for the multi-label classification task. The primitive feature selection for multi-label learning and the feature description of visual geometric elements are conducted individually to match similar PGFs. The experiment results show the competitive performance of the proposed method compared with existing PGF retrieval methods in terms of both time consumption and retrieval quality.

Paper Details

Date Published: 8 February 2015
PDF: 11 pages
Proc. SPIE 9402, Document Recognition and Retrieval XXII, 94020N (8 February 2015); doi: 10.1117/12.2075848
Show Author Affiliations
Songping Fu, Peking Univ. (China)
Xiaoqing Lu, Peking Univ. (China)
State Key Laboratory of Digital Publishing Technology (China)
Lu Liu, Peking Univ. (China)
Jingwei Qu, Peking Univ. (China)
Zhi Tang, Peking Univ. (China)
State Key Laboratory of Digital Publishing Technology (China)

Published in SPIE Proceedings Vol. 9402:
Document Recognition and Retrieval XXII
Eric K. Ringger; Bart Lamiroy, Editor(s)

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