Machine Learning for Visual Concept Recognition and Ranking for Images

Alexander Binder, Wojciech Samek, Klaus Muller, Motoaki Kawanabe

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

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.

Original languageEnglish
Title of host publicationCognitive Technologies
PublisherSpringer Verlag
Pages211-223
Number of pages13
Volume39
ISBN (Print)9783319067544
DOIs
Publication statusPublished - 2014 Jan 1

Publication series

NameCognitive Technologies
Volume39
ISSN (Print)16112482

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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  • Cite this

    Binder, A., Samek, W., Muller, K., & Kawanabe, M. (2014). Machine Learning for Visual Concept Recognition and Ranking for Images. In Cognitive Technologies (Vol. 39, pp. 211-223). (Cognitive Technologies; Vol. 39). Springer Verlag. https://doi.org/10.1007/978-3-319-06755-1_17