Binder, A.A.BinderSamek, W.W.SamekKloft, M.M.KloftMüller, C.C.MüllerMüller, K.-R.K.-R.MüllerKawanabe, M.M.Kawanabe2022-03-132022-03-132011https://publica.fraunhofer.de/handle/publica/3962662-s2.0-84922032448In this paper we present details on the joint submission of TU Berlin and Fraunhofer FIRST to the ImageCLEF 2011 Photo Annotation Task.We sought to experiment with extensions of Bag-of-Words (BoW) models at several levels and to apply several kernel-based learning methods recently developed in our group. For classifier training we used non-sparse multiple kernel learning (MKL) and an efficient multi-task learning (MTL) heuristic based on MKL over kernels from classifier outputs. For the multi-modal fusion we used a smoothing method on tag-based features inspired by Bag-of-Words soft mappings and Markov random walks. We submitted one multi-modal run extended by the user tags and four purely visual runs based on Bag-of-Words models. Our best visual result which used the MTL method was ranked first according to mean average precision (MAP) within the purely visual submissions. Our multi-modal submission achieved the first rank by MAP among the multi-modal submissions and t he best MAP among all submissions. Submissions by other groups such as BPACAD, CAEN, UvA-ISIS, LIRIS were ranked closely.enconference paper