Using latent semantic analysis to improve access to textual information
CHI '88 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
SimFusion: measuring similarity using unified relationship matrix
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Early versus late fusion in semantic video analysis
Proceedings of the 13th annual ACM international conference on Multimedia
Early versus late fusion in semantic video analysis
Proceedings of the 13th annual ACM international conference on Multimedia
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Active post-refined multimodality video semantic concept detection with tensor representation
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Multi-modality video shot clustering with tensor representation
Multimedia Tools and Applications
Tensor-based transductive learning for multimodality video semantic concept detection
IEEE Transactions on Multimedia
| Hi-index | 0.00 |
Interaction and integration of multi-modality media types such as visual, audio and textual data in video are the essence of video content analysis. Although any uni-modality type partially expresses limited semantics less or more, video semantics are fully manifested only by interaction and integration of any unimodal. A great deal of research has been focused on utilizing multi-modality features for better understanding of video semantics. In this paper, we propose a new approach to detect semantic concept in video using SimFusion and Locality Preserving Projections (LPP) from temporal-sequenced associated cooccuring multimodal media data in video. SimFusion is an effective algorithm to reinforce or propagate the similarity relations between multi-modalities. LPP is an optimal combination of linear and nonlinear dimensionality reduction method. Our experiments show that by employing the two key techniques, we can improve the performance of video semantic concept detection.