@Article{Taschwer2016a, author = {Taschwer, Mario and Marques, Oge}, journal = {Multimedia Tools and Applications}, title = {Automatic Separation of Compound Figures in Scientific Articles}, year = {2016}, issn = {1573-7721}, month = {dec}, pages = {1--30}, 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 (ComFig) to automatically classify and separate compound figures, which consists of two main steps: (i) a supervised compound figure classifier (ComFig classifier) 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 (ComFig separation). The proposed ComFig classifier is shown to achieve state-of-the-art classification performance on a published dataset. Our ComFig separation 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 ComFig chain combining classifier and separation algorithm, and use it to optimize the misclassification loss of the ComFig classifier for maximal effectiveness in the chain.}, address = {New York}, doi = {10.1007/s11042-016-4237-x}, language = {EN}, pdf = {https://www.itec.aau.at/bib/files/fig-separation-mtap.pdf}, publisher = {Springer} }