Compound Figure Separation at ImageCLEF 2015

We participated in the compound figure separation subtask of the ImageCLEF 2015 medical classification task (group AAUITEC). The paper describing our approach has been accepted for the CLEF 2015 Working Notes. A preprint version is available here.

Update: I presented a poster at CLEF 2015 in Toulouse on September 9, 2015. The paper appeared online in CEUR Workshop Proceedings. Here is the BibTeX citation:

@InProceedings{Taschwer2015,
Title                    = {{AAUITEC} at {ImageCLEF} 2015: Compound Figure Separation},
Author                   = {Taschwer, Mario and Marques, Oge},
Booktitle                = {{CLEF} 2015 Working Notes},
Year                     = {2015},
Month                    = {September},
Series                   = {CEUR Workshop Proceedings, ISSN 1613-0073},
Volume                   = {1391},
Location                 = {Toulouse, France},
Url                      = {http://ceur-ws.org/Vol-1391/25-CR.pdf}
}

Abstract:
Our approach to automatically separating compound figures appearing in biomedical articles is split into two image processing algorithms: one is based on detecting separator edges, and the other tries to identify background bands separating subfigures. Only one algorithm is applied to a given image, according to the prediction of a binary classifier trained to distinguish graphical illustrations from other images in biomedical articles. Our submission to the ImageCLEF 2015 compound figure separation task achieved an accuracy of 49% on the provided test set of about 3400 compound images. This stays clearly behind the best submission of other participants (85% accuracy), but is by an order of magnitude faster than other approaches reported in the literature.