Structural SVM with Partial Ranking for Activity Segmentation and Classification

Structural SVM is an extension of the support vector machine for the joint prediction of structured labels from multiple measurements. Following a large margin principle, the training of structural SVM ensures that the ground-truth labeling of each sample receives a score higher than that of any other labeling. However, no specific score ranking is imposed among the other labelings. In this letter, we extend the standard constraint set of structural SVM with constraints between “almost-correct” labelings and less desirable ones to obtain a partial-ranking structural SVM (PR-SSVM) approach. Experimental results on action segmentation and classification with two challenging datasets (the TUM Kitchen mocap dataset and the CMU-MMAC video dataset) show that the proposed method achieves better detection and false alarm rates and higher F1 scores than both the conventional structural SVM and a comparable unstructured predictor. The proposed method also achieves higher accuracy than the state of the art on these datasets in excess of 14 and 31 percentage points, respectively.