Machine Learning
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient greedy learning of Gaussian mixture models
Neural Computation
Partially Supervised Classification of Text Documents
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
PEBL: Web Page Classification without Negative Examples
IEEE Transactions on Knowledge and Data Engineering
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Learning the semantics of multimedia queries and concepts from a small number of examples
Proceedings of the 13th annual ACM international conference on Multimedia
Text Classification without Negative Examples Revisit
IEEE Transactions on Knowledge and Data Engineering
IEEE Intelligent Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluation campaigns and TRECVid
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Learning concepts from large scale imbalanced data sets using support cluster machines
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Rough set Based Ensemble Classifier forWeb Page Classification
Fundamenta Informaticae
A note on Platt's probabilistic outputs for support vector machines
Machine Learning
A 3-dimensional sift descriptor and its application to action recognition
Proceedings of the 15th international conference on Multimedia
Evaluating MapReduce for Multi-core and Multiprocessor Systems
HPCA '07 Proceedings of the 2007 IEEE 13th International Symposium on High Performance Computer Architecture
Hadoop: The Definitive Guide
Evaluating Color Descriptors for Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
EMD-based video clip retrieval by many-to-many matching
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
A quick search method for audio and video signals based on histogram pruning
IEEE Transactions on Multimedia
Multimedia event-based video indexing using time intervals
IEEE Transactions on Multimedia
Learning from examples in the small sample case: face expression recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Constructing and Utilizing Video Ontology for Accurate and Fast Retrieval
International Journal of Multimedia Data Engineering & Management
| Hi-index | 0.00 |
This paper develops a query-by-example method for retrieving shots of an event (event shots) using example shots provided by a user. The following three problems are mainly addressed. Firstly, event shots cannot be retrieved using a single model as they contain significantly different features due to varied camera techniques, settings and so forth. This is overcome by using rough set theory to extract multiple classification rules with each rule specialized to retrieve a portion of event shots. Secondly, since a user can only provide a small number of example shots, the amount of event shots retrieved by extracted rules is inevitably limited. We thus incorporate bagging and the random subspace method. Classifiers characterize significantly different event shots depending on example shots and feature dimensions. However, this can result in the potential retrieval of many unnecessary shots. Rough set theory is used to combine classifiers into rules which provide greater retrieval accuracy. Lastly, counter example shots, which are a necessity for rough set theory, are not provided by the user. Hence, a partially supervised learning method is used to collect these from shots other than example shots. Counter example shots, which are as similar to example shots as possible, are collected because they are useful for characterizing the boundary between event shots and the remaining shots. The proposed method is tested on TRECVID 2009 video data.