Compositional Models for Video Event Detection: A Multiple Kernel Learning Latent Variable Approach (Open Access)
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.
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 03, 2014
- Accession Number
- AD1037599
Entities
People
- Arash Vahdat
- Greg Mori
- Ilseo Kim
- Kevin Cannons
- Sangmin Oh
Organizations
- Simon Fraser University