Compound Figure Separation at ImageCLEF 2015

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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:

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                      = {}

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.