A diagram retrieval method with multi-label learning

Songping Fu; Xiaoqing Lu; Lu Liu; Jingwei Qu; Zhi Tang

doi:10.1117/12.2075848

8 February 2015 A diagram retrieval method with multi-label learning

Songping Fu, Xiaoqing Lu, Lu Liu, Jingwei Qu, Zhi Tang

Proceedings Volume 9402, Document Recognition and Retrieval XXII; 94020N (2015) https://doi.org/10.1117/12.2075848
Event: SPIE/IS&T Electronic Imaging, 2015, San Francisco, California, United States

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.

Citation Download Citation

Songping Fu, Xiaoqing Lu, Lu Liu, Jingwei Qu, and Zhi Tang "A diagram retrieval method with multi-label learning", Proc. SPIE 9402, Document Recognition and Retrieval XXII, 94020N (8 February 2015); https://doi.org/10.1117/12.2075848

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