Prof. Corso moved to the Electrical Engineering and Computer Science department at the University of Michigan in the 8/2014. He continues his work and research group in high-level computer vision at the intersection of perception, semantics/language, and robotics. Unless you are looking for something specific, historically, here, you probably would rather go to his new page.
Jason J. Corso
Research Pages
Snippets by Topic
* Active Clustering
* Activity Recognition
* Medical Imaging
* Metric Learning
* Semantic Segmentation
* Video Segmentation
* Video Understanding
Selected Project Pages
* Action Bank
* LIBSVX: Supervoxel Library and Evaluation
* Brain Tumor Segmentation
* CAREER: Generalized Image Understanding
* Summer of Code 2010: The Visual Noun
* ACE: Active Clustering
* ISTARE: Intelligent Spatiotemporal Activity Reasoning Engine
* GBS: Guidance by Semantics
* Semantic Video Summarization
Data Sets
* YouCook
* Chen
* UB/College Park Building Facades
Other Information
* Code/Data Downloads
* List of Grants
Semantic Video Summarization
People: Jason Corso (PI)

Funding Agency: CIA. This project is funded through an IC Postdoctoral Fellowship program. See for more information.

This project is kicking off in January 2011.

Analysis of massive multimedia collections is at the core of the intelligence community today and will be increasingly so in the future. Video data is being collected at alarming rates and yet there exists no comprehensive forensic toolset that enables the intelligence analyst to quickly exploit and analyze video in the context of the massive collections. Sifting through hours of video to find a needle is laborious and error-prone. Video analysis needs to happen at the semantic level to facilitate efficient and effective exploitation. We pose and investigate the semantic video summarization problem, which requires a joint solution to semantic entity extraction, entity-entity relationship extraction, dynamic event recognition, and video categorization.

We investigate an approach for semantic summarization of video content across massive collections. Our approach is grounded in formal ontology—indeed the semantics we use to capture the domain entities and how they interrelate—but this ontology is jointly induced from the data and established by the human domain experts (i.e., interactive machine learning). The ontology is rigorously married to the underlying statistical mathematical representation (a multilevel Markov network) and inference is automatic on a given video.

last updated: Tue Jul 29 10:11:58 2014; copyright jcorso