Fraunhofer FIRST’s Submission to ImageCLEF2009 Photo Annotation Task: Non-sparse Multiple Kernel Learning

In order to achieve good performance in image annotation tasks, it is necessary to com-bine information from various image features. In our submission, we applied the non-sparse multiple kernel learning for feature combination proposed by Kloft et al.(2009) to the ImageCLEF2009 photo annotation data. Since some of the concepts of the Im-ageCLEF task are rather abstract, we conjectured that color histograms are informative for some categories such as sky and snow. Therefore we tried pyramid histograms of pixel colors. Since the images are not aligned, we sorted histograms at different places, when computing similarity of two images. Short description of our methods will be presented and obtained results will be discussed in this manuscript