BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Large-Scale Parallel Data Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Accelerating exact k-means algorithms with geometric reasoning
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Clustering Algorithms
Modern Information Retrieval
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Comparison of Affine Region Detectors
International Journal of Computer Vision
YALE: rapid prototyping for complex data mining tasks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Evaluation campaigns and TRECVid
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Image Processing, Analysis, and Machine Vision
Image Processing, Analysis, and Machine Vision
Introduction to Clustering Large and High-Dimensional Data
Introduction to Clustering Large and High-Dimensional Data
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
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The paper deals with an application of clustering we used as one of data reduction methods included in processing huge amount of video data provided for TRECVid evaluations. The problem we solved by means of clustering was to partition the local feature descriptors space so that thousands of partitions represent visual words, which may be effectively employed in video retrieval using classical information retrieval techniques. It has proved that well-known algorithms as K-means do not work well in this task or their computational complexity is too high. Therefore we developed a simple clustering method (referred to as MLD) that partitions the high-dimensional feature space incrementally in one to two database scans. The paper describes the problem of video retrieval and the role of clustering in the process, the MLD method and experiments focused on comparison with other clustering methods in the video retrieval application context.