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.