SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rule-based video classification system for basketball video indexing
MULTIMEDIA '00 Proceedings of the 2000 ACM workshops on Multimedia
Feature selection for high-dimensional genomic microarray data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Challenges of Image and Video Retrieval
CIVR '02 Proceedings of the International Conference on Image and Video Retrieval
Video Retrieval by Feature Learning in Key Frames
CIVR '02 Proceedings of the International Conference on Image and Video Retrieval
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Algorithms for Feature Selection: An Evaluation
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
Image classification for content-based indexing
IEEE Transactions on Image Processing
A Stochastic Algorithm for Feature Selection in Pattern Recognition
The Journal of Machine Learning Research
The state of the art in image and video retrieval
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
Interactive multimedia system for distance learning of higher education
Edutainment'06 Proceedings of the First international conference on Technologies for E-Learning and Digital Entertainment
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Feature selection for video retrieval applications is impractical with existing techniques, because of their high time complexity and their failure on the relatively sparse training data that is available given video data size. In this paper we present a novel heuristic method for selecting image features for video, called the Complement Sort-Merge Tree (CSMT). It combines the virtues of a wrapper model approach for better accuracy with those of a filter method approach for incrementally deriving the appropriate features quickly. A novel combination of Fastmap for dimensionality reduction and Mahalanobis distance for likelihood determination is used as the induction algorithm. The time cost of CSMT is linear in the number of features and in the size of the training set, which is very reasonable. We apply CSMT to the domain of fast video retrieval of extended (75 minutes) instructional videos, and demonstrate its high accuracy in classifying frames.