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Collective Activity Detection using Hinge-loss Markov Random Fields
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@MISC{London_collectiveactivity,
author = {Ben London and Sameh Khamis and Stephen H. Bach and Bert Huang and Lise Getoor and Larry Davis},
title = {Collective Activity Detection using Hinge-loss Markov Random Fields},
year = {}
}
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Abstract
We propose hinge-loss Markov random fields (HL-MRFs), a powerful class of continuous-valued graphical models, for high-level computer vision tasks. HL-MRFs are characterized by log-concave density functions, and are able to perform efficient, exact inference. Their templated hinge-loss potential functions naturally encode soft-valued logical rules. Using the declarative modeling language probabilistic soft logic, one can easily define HL-MRFs via familiar constructs from first-order logic. We apply HL-MRFs to the task of activity detection, using principles of collective classification. Our model is simple, intuitive and interpretable. We evaluate our model on two datasets and show that it achieves significant lift over the low-level detectors. 1.
Keyphrases
hinge-loss markov random field collective activity detection activity detection low-level detector high-level computer vision task templated hinge-loss potential function continuous-valued graphical model familiar construct collective classification significant lift soft-valued logical rule log-concave density function exact inference powerful class first-order logic
