Robust Scene Categorization by Learning Image Statistics in Context
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
The challenge problem for automated detection of 101 semantic concepts in multimedia
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Paired Comparisons Method for Solving Multi-Label Learning Problem
HIS '06 Proceedings of the Sixth International Conference on Hybrid Intelligent Systems
Vehicle Categorization: Parts for Speed and Accuracy
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
A reranking approach for context-based concept fusion in video indexing and retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
Random k-Labelsets: An Ensemble Method for Multilabel Classification
ECML '07 Proceedings of the 18th European conference on Machine Learning
Foundations and Trends in Information Retrieval
Exploring inter-concept relationship with context space for semantic video indexing
Proceedings of the ACM International Conference on Image and Video Retrieval
Multimedia data mining: state of the art and challenges
Multimedia Tools and Applications
Representations of Keypoint-Based Semantic Concept Detection: A Comprehensive Study
IEEE Transactions on Multimedia
| Hi-index | 0.00 |
Videos have become an integral part of our life, from watching movies online to the use of videos in classroom teaching. Existing machine learning techniques are constrained with this scaled up activity because of this huge upsurge in online activity. A lot of research is now focused on reducing the time and accuracy of video classification. Content-Based Video Information Retrieval CBVIR implementation (E.g. Columbia374) is one such approach. We propose a fast Hamming Selection Pruned Sets (HSPS) algorithm that efficiently transforms multi-label video dataset into single-label representation. Thus, multi-label relationship between the labels can be retained for later single label classifier learning stage. Hamming distance (HD) is used to detect similarity between label-sets. HSPS captures new potential label-set relationships that were previously undetected by baseline approach. Experiments show a significant 22.9% dataset building time reduction and consistent accuracy improvement over the baseline method. HSPS also works on general multi-label dataset.