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Compositional Models for Video Event Detection: A Multiple Kernel Learning Latent Variable Approach
2013
2013 IEEE International Conference on Computer Vision
We present a compositional model for video event detection. A video is modeled using a collection of both global and segment-level features and kernel functions are employed for similarity comparisons. The locations of salient, discriminative video segments are treated as a latent variable, allowing the model to explicitly ignore portions of the video that are unimportant for classification. A novel, multiple kernel learning (MKL) latent support vector machine (SVM) is defined, that is used to
doi:10.1109/iccv.2013.463
dblp:conf/iccv/VahdatCMOK13
fatcat:rgxl3u4kdnh5bmox2xdzxhxflq