Automatic Separation of Compound Figures in Scientific Articles
This addresses a bottleneck in content-based analysis for researchers and librarians by improving image retrieval from scientific articles, though it is incremental as it builds on existing methods.
The paper tackles the problem of analyzing and retrieving images in scientific articles by automatically classifying and separating compound figures, achieving state-of-the-art classification performance and superior separation accuracy on multiple datasets.
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.