A reward-and-punishment-based approach for concept detection using adaptive ontology rules

Authors:
Chidansh A. Bhatt;Pradeep K. Atrey;Mohan S. Kankanhalli
Affiliations:
National University of Singapore, Singapore;University of Winnipeg, Canada;National University of Singapore, Singapore
Venue:
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Year:
2013

Citing 25
Cited 0

Discovery and Segmentation of Activities in Video

IEEE Transactions on Pattern Analysis and Machine Intelligence
Decision Theory: An Introduction to Dynamic Programming and Sequential Decisions

Decision Theory: An Introduction to Dynamic Programming and Sequential Decisions
Semantic annotation of soccer videos: automatic highlights identification

Computer Vision and Image Understanding - Special isssue on video retrieval and summarization
A PCA-based similarity measure for multivariate time series

Proceedings of the 2nd ACM international workshop on Multimedia databases
Multimedia semantic indexing using model vectors

ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
Evaluation campaigns and TRECVid

MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Model-shared subspace boosting for multi-label classification

Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Classification of video events using 4-dimensional time-compressed motion features

Proceedings of the 6th ACM international conference on Image and video retrieval
Building a comprehensive ontology to refine video concept detection

Proceedings of the international workshop on Workshop on multimedia information retrieval
Correlative multi-label video annotation

Proceedings of the 15th international conference on Multimedia
Video Event Recognition Using Kernel Methods with Multilevel Temporal Alignment

IEEE Transactions on Pattern Analysis and Machine Intelligence
SIFT-Bag kernel for video event analysis

MM '08 Proceedings of the 16th ACM international conference on Multimedia
Unified video annotation via multigraph learning

IEEE Transactions on Circuits and Systems for Video Technology
On parsing visual sequences with the hidden Markov model

Journal on Image and Video Processing
Beyond distance measurement: constructing neighborhood similarity for video annotation

IEEE Transactions on Multimedia - Special section on communities and media computing
Multimedia Interpretation for Dynamic Ontology Evolution

Journal of Logic and Computation
A cusum change-point detection algorithm for non-stationary sequences with application to data network surveillance

Journal of Systems and Software
Investigating fuzzy DLs-based reasoning in semantic image analysis

Multimedia Tools and Applications
Video Annotation and Retrieval Using Ontologies and Rule Learning

IEEE MultiMedia
Parsimonious linear fingerprinting for time series

Proceedings of the VLDB Endowment
Event detection and recognition for semantic annotation of video

Multimedia Tools and Applications
Multimedia data mining: state of the art and challenges

Multimedia Tools and Applications
Video Semantic Event/Concept Detection Using a Subspace-Based Multimedia Data Mining Framework

IEEE Transactions on Multimedia
Knowledge-assisted semantic video object detection

IEEE Transactions on Circuits and Systems for Video Technology
Event detection in field sports video using audio-visual features and a support vector Machine

IEEE Transactions on Circuits and Systems for Video Technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

Despite the fact that performance improvements have been reported in the last years, semantic concept detection in video remains a challenging problem. Existing concept detection techniques, with ontology rules, exploit the static correlations among primitive concepts but not the dynamic spatiotemporal correlations. The proposed method rewards (or punishes) detected primitive concepts using dynamic spatiotemporal correlations of the given ontology rules and updates these ontology rules based on the accuracy of detection. Adaptively learned ontology rules significantly help in improving the overall accuracy of concept detection as shown in the experimental result.