Dimensionality reduction for descriptor generation in rushes editing

Rushes editing, which enables the computer to edit the film like a professional film cutter based on the noisy and redundant footage, is an active topic in multimedia semantic analysis. The most critical problem of rushes editing is how to generate an effective, efficient, and robust descriptor for the footage content analysis. This paper proposes a novel non-linear dimensionality reduction algorithm called Multi-Layer Isometric Feature Mapping (ML-Isomap) for automatic descriptor generation. First, a K-nearest Neighbor Based Clustering (KNBC) algorithm is utilized to partition the high-dimensional data points into a set of data blocks. Second, intra-cluster graphs are constructed based on the individual character of each data block to build the basic layer for the ML-Isomap. Third, the inter-cluster graph is constructed by analyzing the interrelation among these isolated data blocks to build the hyper-layers for the ML-Isomap. Finally, all the data points are mapped into the unique low-dimensional feature space by keeping the corresponding relations of the multiple layers in the high-dimensional feature space to the greatest extent. The comparative experiments on synthetic data as well as the real rushes editing tasks demonstrate that the proposed algorithm can generate the effective descriptor with much lower dimensions for the semantic video analysis.