Learning concept bundles for video search with complex queries
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Inconsistency-based active learning for support vector machines
Pattern Recognition
Training support vector machine through redundant data reduction
Proceedings of the 4th International Conference on Internet Multimedia Computing and Service
Personalized video recommendation based on viewing history with the study on YouTube
Proceedings of the 4th International Conference on Internet Multimedia Computing and Service
Point-context descriptor based region search for logo recognition
Proceedings of the 4th International Conference on Internet Multimedia Computing and Service
Proceedings of the 20th ACM international conference on Multimedia
Robust cross-media transfer for visual event detection
Proceedings of the 20th ACM international conference on Multimedia
Combining SIFT and global features for web image classification
PCM'12 Proceedings of the 13th Pacific-Rim conference on Advances in Multimedia Information Processing
Interactive social group recommendation for Flickr photos
Neurocomputing
Effective transfer tagging from image to video
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Literature survey of active learning in multimedia annotation and retrieval
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
Latent feature learning in social media network
Proceedings of the 21st ACM international conference on Multimedia
q-Gaussian mixture models for image and video semantic indexing
Journal of Visual Communication and Image Representation
Effective automatic image annotation via integrated discriminative and generative models
Information Sciences: an International Journal
Hi-index | 0.00 |
Video indexing, also called video concept detection, has attracted increasing attentions from both academia and industry. To reduce human labeling cost, active learning has been introduced to video indexing recently. In this paper, we propose a novel active learning approach based on the optimum experimental design criteria in statistics. Different from existing optimum experimental design, our approach simultaneously exploits sample's local structure, and sample relevance, density, and diversity information, as well as makes use of labeled and unlabeled data. Specifically, we develop a local learning model to exploit the local structure of each sample. Our assumption is that for each sample, its label can be well estimated based on its neighbors. By globally aligning the local models from all the samples, we obtain a local learning regularizer, based on which a local learning regularized least square model is proposed. Finally, a unified sample selection approach is developed for interactive video indexing, which takes into account the sample relevance, density and diversity information, and sample efficacy in minimizing the parameter variance of the proposed local learning regularized least square model. We compare the performance between our approach and the state-of-the-art approaches on the TREC video retrieval evaluation (TRECVID) benchmark. We report superior performance from the proposed approach.