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Machine learning for visual concept recognition and ranking for images

: Binder, A.; Samek, W.; Müller, K.-R.; Kawanabe, M.


Wahlster, Wolfgang (Hrsg.); Grallert, H.-J.; Wess, S.; Friedrich, H.; Widenka, T.:
Towards the Internet of Services. The THESEUS Research Program
Cham: Springer International Publishing, 2014 (Cognitive technologies)
ISBN: 978-3-319-06754-4 (Print)
ISBN: 978-3-319-06755-1 (Online)
DOI: 10.1007/978-3-319-06755-1
Aufsatz in Buch
Fraunhofer FIRST ()

Recognition of a large set of generic visual concepts in images and ranking of images based on visual semantics is one of the unsolved tasks for future multimedia and scientific applications based on image collections. From that perspective, improvements of the quality of semantic annotations for image data are well matched to the goals of the THESEUS research program with respect to multimedia and scientific services. We will introduce the data-driven and algorithmic challenges inherent in such tasks from a perspective of statistical data analysis and machine learning and discuss approaches relying on kernel-based similarities and discriminative methods which are capable of processing large-scale datasets.