PicSOM Experiments in TRECVID 2006
Mats Sjöberg, Satoru Ishikawa, Markus Koskela, Jorma T. Laaksonen, Erkki Oja · Aaltodoc (Aalto University) · 2006
Our experiments in TRECVID 2010 include participation in the semantic indexing and known-item search tasks. In the semantic indexing task we implemented SVM-based classifiers on five different low-level visual features extracted from the keyframes. In addition to the main keyframes provided by NIST, we also extracted and analysed additional frames from longer shots. The feature-wise classifiers were fused using standard and weighted geometric mean. We submitted the following four runs: • PicSOM_geom: Geometric mean of five features, all keyframes. • PicSOM_wgeom: Weighted geometric mean of five features, all keyframes. • PicSOM_2geom-mkf: Geometric mean of two “best ” features, main keyframe only. • PicSOM_2geom-max: Geometric mean of two “best ” features, all keyframes. The runs 2geom-max and wgeom obtained the highest MIAP scores (with essentially the same score, 0.0697 vs. 0.0694). Overall, using more keyframes always improved the results substantially. Our weighting approach improved the result over the standard geometric mean. However, by using only two features in fusion without weighting we achieved a similar result. In the known-item search task we submitted two automatic and two interactive runs: • PicSOM_1: Text search + concept detectors with distribution