  Content-based analysis and retrieval of digital images found in scientific
articles is often hindered by images consisting of multiple subfigures
(compound figures). We address this problem by proposing a method to
automatically classify and separate compound figures, which consists of two
main steps: (i) a supervised compound figure classifier (CFC) discriminates
between compound and non-compound figures using task-specific image features;
and (ii) an image processing algorithm is applied to predicted compound images
to perform compound figure separation (CFS). Our CFC approach is shown to
achieve state-of-the-art classification performance on a published dataset. Our
CFS algorithm shows superior separation accuracy on two different datasets
compared to other known automatic approaches. Finally, we propose a method to
evaluate the effectiveness of the CFC-CFS process chain and use it to optimize
the misclassification loss of CFC for maximal effectiveness in the process
chain.
