  Scientific literature contains large volumes of unstructured data,with over
30\% of figures constructed as a combination of multiple images, these compound
figures cannot be analyzed directly with existing information retrieval tools.
In this paper, we propose a semantic segmentation approach for compound figure
separation, decomposing the compound figures into "master images". Each master
image is one part of a compound figure governed by a subfigure label (typically
"(a), (b), (c), etc"). In this way, the separated subfigures can be easily
associated with the description information in the caption. In particular, we
propose an anchor-based master image detection algorithm, which leverages the
correlation between master images and subfigure labels and locates the master
images in a two-step manner. First, a subfigure label detector is built to
extract the global layout information of the compound figure. Second, the
layout information is combined with local features to locate the master images.
We validate the effectiveness of proposed method on our labeled testing dataset
both quantitatively and qualitatively.
