International Conference on Multimedia Modeling

MultiMedia Modeling pp 874-885 | Cite as

Ordering of Visual Descriptors in a Classifier Cascade Towards Improved Video Concept Detection

  • Foteini Markatopoulou
  • Vasileios Mezaris
  • Ioannis Patras
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9516)

Abstract

Concept detection for semantic annotation of video fragments (e.g. keyframes) is a popular and challenging problem. A variety of visual features is typically extracted and combined in order to learn the relation between feature-based keyframe representations and semantic concepts. In recent years the available pool of features has increased rapidly, and features based on deep convolutional neural networks in combination with other visual descriptors have significantly contributed to improved concept detection accuracy. This work proposes an algorithm that dynamically selects, orders and combines many base classifiers, trained independently with different feature-based keyframe representations, in a cascade architecture for video concept detection. The proposed cascade is more accurate and computationally more efficient, in terms of classifier evaluations, than state-of-the-art classifier combination approaches.

Keywords

Concept detection Video analysis Cascade architecture Classifier ordering 

Notes

Acknowledgements

This work was supported by the European Commission under contract FP7-600826 ForgetIT.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Foteini Markatopoulou
    • 1
    • 2
  • Vasileios Mezaris
    • 1
  • Ioannis Patras
    • 2
  1. 1.Information Technologies Institute (ITI)CERTHThermiGreece
  2. 2.Queen Mary University of LondonLondonUK

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