Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/18070
Title: Dimensionality reduction for heterogeneous dataset in rushes editing
Authors: Liu, Y
Liu, Y 
Chan, KCC 
Keywords: Dimensionality reduction
Isometric feature mapping
Manifold learning
Multi-layer Isometric feature mapping
Rushes editing
Issue Date: 2009
Publisher: Elsevier
Source: Pattern recognition, 2009, v. 42, no. 2, p. 229-242 How to cite?
Journal: Pattern recognition 
Abstract: Rushes editing enables the computer to edit the film like a professional film cutter based on the raw footage. The most important issue in rushes editing is the generation of the effective, efficient, and robust descriptors for footage content analysis. Dimensionality reduction technology provides the means to generate such descriptors by seeking a low-dimensional equivalence of the high-dimensional video data using intelligent algorithms. However, existing dimensionality reduction techniques are not directly applicable to the editing of rushes because of the heterogeneity of rushes data. To deal with this heterogeneity, this paper proposes a novel non-linear dimensionality reduction algorithm called multi-layer isometric feature mapping (ML-Isomap). First, a clustering algorithm is utilized to partition the high-dimensional data points into a set of data blocks in the high-dimensional feature space. 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 maintaining to the greatest extent the corresponding relations of the multiple layers in the high-dimensional feature space. Comparative experiments on synthetic data as well as real rushes editing tasks demonstrate that the proposed algorithm can reduce the dimensions of various datasets efficiently while preserving both the global structure and the local details of the heterogeneous dataset.
URI: http://hdl.handle.net/10397/18070
ISSN: 0031-3203
EISSN: 1873-5142
DOI: 10.1016/j.patcog.2008.06.016
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