Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  studies automatic segmentation of multiple motions from tracked feature points through spectral embedding and clustering of linear subspaces.  show that the dimension of the ambient space is crucial for separability, and that low dimensions chosen in prior work are not optimal.  suggest lor and upper bounds together with a data-driven procedure for choosing the optimal ambient dimension. Application of our approach to the Hopkins155 video benchmark database uniformly outperforms a range of state-of-the-art methods both in terms of segmentation accuracy and computational speed.
['Fabien Lauer', 'Christoph Schnörr']
I am writing a research paper on a idea Spectral clustering of linear subspaces for motion segmentation
[{"from": "human", "value": "I am writing a research paper on a idea Spectral clustering of linear subspaces for motion segmentation"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  studies automatic segmentation of multiple motions from tracked feature points through spectral embedding and clustering of linear subspaces.  show that the dimension of the ambient space is crucial for separability, and that low dimensions chosen in prior work are not optimal.  suggest lor and upper bounds together with a data-driven procedure for choosing the optimal ambient dimension. Application of our approach to the Hopkins155 video benchmark database uniformly outperforms a range of state-of-the-art methods both in terms of segmentation accuracy and computational speed."}]