UDEL CIS Working Notes in ImageCLEF 2016

Figures play an important role within biomedical publications. A typical and essential first step toward using images is the detection of compound figures and their separation into panels. In ImageCLEF’16 our team has participated in the compound figure detection and separation tasks, where we utilized a method based on connected component analysis (CCA) to detect and to separate compound figures, while extending CCA in several ways to improve correct detection of subfigures while avoiding over-fragmentation. We have also participated in the Subfigure Classification task, where we employed an array of global image characteristics and a merge-split strategy coupled with neural network classifiers. We describe here the methods used in each task and analyze the performance of our system.