Sequential labeling with structural SVM under an average precision loss

Publisher:
Springer
Publication Type:
Conference Proceeding
Citation:
Structural, Syntactic, and Statistical Pattern Recognition, 2016, 10029 LNCS pp. 344 - 354
Issue Date:
2016-01-01
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© Springer International Publishing AG 2016.The average precision (AP) is an important and widelyadopted 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.
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