FHDO Biomedical Computer Science Group at Medical Classification Task of ImageCLEF 2015.
Obioma Pelka, Christoph M. Friedrich · CLEF (Working Notes) · 2015
This paper presents the modelling approaches performed by the FHDO Biomedical Computer Science Group for the compound figure detection and subfigure classification tasks at ImageCLEF 2015 medical classification. This is the first participation of the group at an accepted lab of the Cross Language Evaluation Forum. For image visual representation, various state-of-the-art visual features such as Bag-of-Keypoints computed with dense SIFT descriptors and the new Border Profile presented in this work, were adopted. Textual representation was obtained by vector quantisation on Bag-of-Words codebook generated using attribute importance derived from χ-test and the Characteristic Delimiters feature presented in this paper. To reduce feature dimension and noise, the principal component analysis was computed separately for all features. Various multiple feature fusion were adopted to supplement visual image information with their corresponding textual information. Random forest models with 100 to 500 deep trees grown by resampling, a multi class linear kernel SVM with C = 0.05 and a late fusion of the two classifiers were used for classification prediction. Six and Eight runs of submission categories: Visual, Textual and Mixed were submitted for the compound figure detection task and subfigure classification task, respectively.