Fast algorithms for projected clustering
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Building Text Classifiers Using Positive and Unlabeled Examples
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
PEBL: Web Page Classification without Negative Examples
IEEE Transactions on Knowledge and Data Engineering
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
Large-Scale Concept Ontology for Multimedia
IEEE MultiMedia
Evaluation campaigns and TRECVid
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Cluster-based data modeling for semantic video search
Proceedings of the 6th ACM international conference on Image and video retrieval
International Journal of Approximate Reasoning
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
A semantic event-detection approach and its application to detecting hunts in wildlife video
IEEE Transactions on Circuits and Systems for Video Technology
Example-based event retrieval in video archive using rough set theory and video ontology
Proceedings of the Tenth International Workshop on Multimedia Data Mining
Video event retrieval from a small number of examples using rough set theory
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part I
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In videos, the same event can be taken by different camera techniques and in different situations. So, shots of the event contain significantly different features. In order to collectively retrieve such shots, we introduce a method which defines an event by using “rough set theory”. Specifically, we extract subsets where shots of the event can be correctly discriminated from all other shots. And, we define the event as the union of subsets. But, to perform the above rough set theory, we need both positive and negative examples. Note that for any possible event, it is impossible to label a huge number of shots as positive or negative. Thus, we adopt a “partially supervised learning” approach where an event is defined from a small number of positive examples and a large number of unlabeled examples. In particular, from unlabeled examples, we collect negative examples based on their similarities to positive ones. Here, to appropriately calculate similarities, we use “subspace clustering” which finds clusters in different subspaces of the high-dimensional feature space. Experimental results on TRECVID 2008 video collection validate the effectiveness of our method.