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

Snippet Topic: Active Clustering

Spectral Active Clustering

Spectral clustering is widely used in data mining, machine learning and pattern recognition. There have been some recent developments in adding pairwise constraints as side information to enforce top-down structure into the clustering results. However, most of these algorithms are "passive" in the sense that the side information is provided beforehand. In this work, we present a spectral active clustering method that actively select pairwise constraints based on a novel notion of node uncertainty rather than pair uncertainty. In our approach, the constraints are used to drive a purification process on the k-nearest neighbor graph---edges are removed from the graph based on the constraints---that ultimately leads to an improved, constraint-satisfied clustering. We have evaluated our framework on three datasets (UCI, gene and image sets) in the context of baseline and state of the art methods and find the proposed algorithm to be superiorly effective.
  C. Xiong, D. Johnson, and J. J. Corso. Spectral active clustering via purification of the k-nearest neighbor graph. In Proceedings of European Conference on Data Mining, 2012. [ bib | .pdf ]
This work is part of our ACE project.

last updated: Tue Jul 29 10:11:57 2014; copyright jcorso