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Sequential Labeling with Structural SVM Under an Average Precision Loss

  • Guopeng Zhang
  • Massimo PiccardiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10029)

Abstract

The average precision (AP) is an important and widely-adopted performance measure for information retrieval and classification systems. However, owing to its relatively complex formulation, very few approaches have been proposed to learn a classifier by maximising its average precision over a given training set. Moreover, most of the existing work is restricted to i.i.d. data and does not extend to sequential data. For this reason, we herewith propose a structural SVM learning algorithm for sequential labeling that maximises an average precision measure. A further contribution of this paper is an algorithm that computes the average precision of a sequential classifier at test time, making it possible to assess sequential labeling under this measure. Experimental results over challenging datasets which depict human actions in kitchen scenarios (i.e., TUM Kitchen and CMU Multimodal Activity) show that the proposed approach leads to an average precision improvement of up to 4.2 and \(5.7\,\%\) points against the runner-up, respectively.

Keywords

Sequential labeling Structural SVM Average precision Loss-augmented inference 

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Global Big Data Technologies CentreUniversity of Technology SydneyUltimoAustralia

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