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Interestingness Prediction by Robust Learning to Rank

  • Yanwei Fu
  • Timothy M. Hospedales
  • Tao Xiang
  • Shaogang Gong
  • Yuan Yao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8690)

Abstract

The problem of predicting image or video interestingness from their low-level feature representations has received increasing interest. As a highly subjective visual attribute, annotating the interestingness value of training data for learning a prediction model is challenging. To make the annotation less subjective and more reliable, recent studies employ crowdsourcing tools to collect pairwise comparisons – relying on majority voting to prune the annotation outliers/errors. In this paper, we propose a more principled way to identify annotation outliers by formulating the interestingness prediction task as a unified robust learning to rank problem, tackling both the outlier detection and interestingness prediction tasks jointly. Extensive experiments on both image and video interestingness benchmark datasets demonstrate that our new approach significantly outperforms state-of-the-art alternatives.

Keywords

Majority Vote Outlier Detection Interestingness Prediction Robust Learn Regularisation Path 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Yanwei Fu
    • 1
  • Timothy M. Hospedales
    • 1
  • Tao Xiang
    • 1
  • Shaogang Gong
    • 1
  • Yuan Yao
    • 2
  1. 1.School of EECSQueen Mary University of LondonUK
  2. 2.School of Mathematical SciencesPeking UniversityChina

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