Video shot motion characterization based on hierarchical overlapped growing neural gas networks
Automatic video shot motion characterization is an important step in video indexing and retrieval after temporal video segmentation. This paper describes a hierarchical overlapped architecture (HOGNG) based upon the growing neural gas (GNG) network [7] to perform this task. The proposed architecture combines the unsupervised and supervised learning schemes in GNG. As higher-level GNGs overlap, the final classification is obtained by fusing the individual classifications generated by the top-level overlapping GNGs. In addition, we employ prefiltering and postfiltering for improving the classification accuracy. Experimental results are presented to show the good classification accuracy of the proposed algorithm on real MPEG video sequences.
growing neural gas video sequence analysis camera motion characterization hierarchical structure multiple classifiers ensemble classifiers self-organizing neural networks overlapped clustering overlapping clusters