Relational Graph Mining for Learning Events from Video

Authors:
Muralikrishna Sridhar;Anthony G. Cohn;David C. Hogg
Affiliations:
University of Leeds, UK, {krishna,agc,dch}@comp.leeds.ac.uk;University of Leeds, UK, {krishna,agc,dch}@comp.leeds.ac.uk;University of Leeds, UK, {krishna,agc,dch}@comp.leeds.ac.uk
Venue:
Proceedings of the 2010 conference on STAIRS 2010: Proceedings of the Fifth Starting AI Researchers' Symposium
Year:
2010

Citing 14
Cited 0

The graph isomorphism problem: its structural complexity

The graph isomorphism problem: its structural complexity
Frequent Subgraph Discovery

ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data

PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
gSpan: Graph-Based Substructure Pattern Mining

ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
State of the art of graph-based data mining

ACM SIGKDD Explorations Newsletter
Mining Graph Data

Mining Graph Data
Unsupervised Activity Perception in Crowded and Complicated Scenes Using Hierarchical Bayesian Models

IEEE Transactions on Pattern Analysis and Machine Intelligence
Integrated Detection and Tracking for Multiple Moving Objects using Data-Driven MCMC Data Association

WMVC '08 Proceedings of the 2008 IEEE Workshop on Motion and video Computing
Learning Functional Object-Categories from a Relational Spatio-Temporal Representation

Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
A novel sequence representation for unsupervised analysis of human activities

Artificial Intelligence
Information theoretical analysis of multivariate correlation

IBM Journal of Research and Development
gPrune: a constraint pushing framework for graph pattern mining

PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Discovering an Event Taxonomy from Video using Qualitative Spatio-temporal Graphs

Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Learning semantic scene models by trajectory analysis

ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this work, we represent complex video activities as one large activity graph and propose a constraint based graph mining technique to discover a partonomy of classes of subgraphs corresponding to event classes. Events are defined as subgraphs of the activity graph that represent what we regard as interesting interactions, that is, where all objects are actively engaged and are characterized by frequent occurrences in the activity graph. Subgraphs with these two properties are mined using a level-wise algorithm, and then partitioned into equivalence classes which we regard as event classes. Moreover, a taxonomy of these event classes naturally emerges from the level-wise mining procedure. Experimental results in an aircraft turnaround apron scenario show that the proposed technique has considerable potential for characterizing and mining events from video.