Accession Number:

AD1037599

Title:

Compositional Models for Video Event Detection: A Multiple Kernel Learning Latent Variable Approach (Open Access)

Descriptive Note:

Conference Paper

Corporate Author:

Simon Fraser University Burnaby Canada

Report Date:

2014-03-03

Pagination or Media Count:

8.0

Abstract:

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 combine and re-weight multiple feature types in a principled fashion while simultaneously operating within the latent variable framework. The compositional nature of the proposed model allows it to respond directly to the challenges of temporal clutter and intra-class variation, which are prevalent in unconstrained internet videos. Experimental results on the TRECVID Multimedia Event Detection 2011 MED11 dataset demonstrate the efficacy of the method.

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Distribution Statement:

APPROVED FOR PUBLIC RELEASE