Automatic Separation of Compound Figures in Scientific Articles

TL;DR

This paper presents an automated method to classify and separate compound figures in scientific articles, improving image analysis and retrieval by accurately distinguishing and splitting multi-part images.

Contribution

It introduces a novel supervised classifier for compound figure detection and an effective separation algorithm, optimizing the process chain for better accuracy.

Findings

Achieved state-of-the-art classification performance.

Demonstrated superior separation accuracy on multiple datasets.

Proposed an evaluation method to optimize the process chain.

Abstract

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.