Large-Scale Concept Ontology for Multimedia
IEEE MultiMedia
Large-scale multimedia semantic concept modeling using robust subspace bagging and MapReduce
LS-MMRM '09 Proceedings of the First ACM workshop on Large-scale multimedia retrieval and mining
IEEE MultiMedia
Visual memes in social media: tracking real-world news in YouTube videos
MM '11 Proceedings of the 19th ACM international conference on Multimedia
IEEE MultiMedia
Designing Category-Level Attributes for Discriminative Visual Recognition
CVPR '13 Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition
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Visual data is exploding! 500 billion consumer photos are taken each year world-wide, 633 million photos taken per year in NYC alone. 120 new video-hours are uploaded on YouTube per minute. The explosion of digital multimedia data is creating a valuable open source for insights. However, the unconstrained nature of 'image/video in the wild' makes it very challenging for automated computer-based analysis. Furthermore, the most interesting content in the multimedia files is often complex in nature reflecting a diversity of human behaviors, scenes, activities and events. To address these challenges, this tutorial will provide a unified overview of the two emerging techniques: Semantic modeling and Massive scale visual recognition, with a goal of both introducing people from different backgrounds to this exciting field and reviewing state of the art research in the new computational era.