Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
An empirical comparison of four initialization methods for the K-Means algorithm
Pattern Recognition Letters
IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
X-means: Extending K-means with Efficient Estimation of the Number of Clusters
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Adaptive dimension reduction for clustering high dimensional data
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Unsupervised clustering of dominant scenes in sports video
Pattern Recognition Letters
Semantic video adaptation based on automatic annotation of sport videos
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
Towards effective indexing for very large video sequence database
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Extracting story units from long programs for video browsing and navigation
ICMCS '96 Proceedings of the 1996 International Conference on Multimedia Computing and Systems
An integrated scheme for automated video abstraction based on unsupervised cluster-validity analysis
IEEE Transactions on Circuits and Systems for Video Technology
| Hi-index | 0.00 |
Due to its tremendous commercial potential, sports video has become a popular research topic nowadays. As the bridge of low-level features and high-level semantic contents, automatic shot clustering is an important issue in the field of sports video content analysis. In previous work, many clustering approaches need some professional knowledge of videos, some experimental parameters, or some thresholds to obtain good clustering results. In this article, we present a new efficient shot clustering algorithm for sports video which is generic and does not need any prior domain knowledge. The novel algorithm, which is called Valid Dimension Clustering(VDC), performs in an unsupervised manner. For the high-dimensional feature vectors of video shots, a new dimensionality reduction approach is proposed first, which takes advantage of the available dimension histogram to get "valid dimensions" as a good approximation of the intrinsic characteristics of data. Then the clustering algorithm performs on valid dimensions one by one to furthest utilize the intrinsic characteristics of each valid dimension. The iterations of merging and splitting of similar shots on each valid dimension are repeated until the novel stop criterion which is designed inheriting the theory of Fisher Discriminant Analysis is satisfied. At last, we apply our algorithm on real video data in our extensive experiments, the results show that VDC has excellent performance and outperforms other clustering algorithms.