The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning the semantics of multimedia queries and concepts from a small number of examples
Proceedings of the 13th annual ACM international conference on Multimedia
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
Rough set Based Ensemble Classifier forWeb Page Classification
Fundamenta Informaticae
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
Query-Based video event definition using rough set theory and high-dimensional representation
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
A quick search method for audio and video signals based on histogram pruning
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
Effectiveness of video ontology in query by example approach
AMT'11 Proceedings of the 7th international conference on Active media technology
Video genre classification using weighted kernel logistic regression
Advances in Multimedia - Special issue on Multimedia Applications for Smart Device in Ubiquitous Environments
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In this paper, we develop an example-based event retrieval method which constructs a model for retrieving events of interest in a video archive, by using examples provided by a user. But, this is challenging because shots of an event are characterized by significantly different features, due to camera techniques, settings and so on. That is, the video archive contains a large variety of shots of the event, while the user can only provide a small number of examples. Considering this, we use "rough set theory" to capture various characteristics of the event. Specifically, by using rough set theory, we can extract classification rules which can correctly identify different subsets of positive examples. Furthermore, in order to extract a larger variety of classification rules, we incorporate "bagging" and "random subspace method" into rough set theory. Here, we define indiscernibility relations among examples based on outputs of classifiers, built on different subsets of examples and different subsets of feature dimensions. Experimental results on TRECVID 2009 video data validate the effectiveness of our example-based event retrieval method.